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Crowdsourcing
Crowdsourcing
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This graphic symbolizes the use of ideas from a wide range of individuals, as used in crowdsourcing.

Crowdsourcing involves a large group of dispersed participants contributing or producing goods or services—including ideas, votes, micro-tasks, and finances—for payment or as volunteers. Contemporary crowdsourcing often involves digital platforms to attract and divide work between participants to achieve a cumulative result. Crowdsourcing is not limited to online activity, however, and there are various historical examples of crowdsourcing. The word crowdsourcing is a portmanteau of "crowd" and "outsourcing".[1][2][3] In contrast to outsourcing, crowdsourcing usually involves less specific and more public groups of participants.[4][5][6]

Advantages of using crowdsourcing include lowered costs, improved speed, improved quality, increased flexibility, and/or increased scalability of the work, as well as promoting diversity.[7][8] Crowdsourcing methods include competitions, virtual labor markets, open online collaboration and data donation.[8][9][10][11] Some forms of crowdsourcing, such as in "idea competitions" or "innovation contests" provide ways for organizations to learn beyond the "base of minds" provided by their employees (e.g. Lego Ideas).[12][13][promotion?] Commercial platforms, such as Amazon Mechanical Turk, match microtasks submitted by requesters to workers who perform them. Crowdsourcing is also used by nonprofit organizations to develop common goods, such as Wikipedia.[14]

Definitions

[edit]

The term crowdsourcing was coined in 2006 by two editors at Wired, Jeff Howe and Mark Robinson, to describe how businesses were using the Internet to "outsource work to the crowd", which quickly led to the portmanteau "crowdsourcing".[15] The Oxford English Dictionary gives a first use: "OED's earliest evidence for crowdsourcing is from 2006, in the writing of J. Howe."[16] The online dictionary Merriam-Webster defines it as: "the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers."[17]

Daren C. Brabham defined crowdsourcing as an "online, distributed problem-solving and production model."[18] Kristen L. Guth and Brabham found that the performance of ideas offered in crowdsourcing platforms are affected not only by their quality, but also by the communication among users about the ideas, and presentation in the platform itself.[19]

Despite the multiplicity of definitions for crowdsourcing, one constant has been the broadcasting of problems to the public, and an open call for contributions to help solve the problem.[original research?] Members of the public submit solutions that are then owned by the entity who originally broadcast the problem. In some cases, the contributor of the solution is compensated monetarily with prizes or public recognition. In other cases, the only rewards may be praise or intellectual satisfaction. Crowdsourcing may produce solutions from amateurs or volunteers working in their spare time, from experts, or from small businesses.[15]

Historical examples

[edit]

While the term "crowdsourcing" was popularized online to describe Internet-based activities,[18] some examples of projects, in retrospect, can be described as crowdsourcing.

Timeline of crowdsourcing examples

[edit]
  • 618–907 – The Tang dynasty of China introduced the joint-stock company, the earliest form of crowdfunding. This was evident during the cold period of the Tang Dynasty when the colder climates resulted in poor harvests and the lessening of agricultural taxes, culminating in the fragmentation of the agricultural sector.[20] The fragmentation meant that the government had to reform the tax system relying more on the taxation of salt and most importantly business leading to the creation of the Joint-Stock Company.[20]
  • 1567 – King Philip II of Spain offered a cash prize for calculating the longitude of a vessel while at sea.[21]
  • 1714 – The longitude rewards: When the British government was trying to find a way to measure a ship's longitudinal position, they offered the public a monetary prize to whoever came up with the best solution.[22][23]
  • 1783 – King Louis XVI offered an award to the person who could "make the alkali" by decomposing sea salt by the "simplest and most economic method".[22]
  • 1848 – Matthew Fontaine Maury distributed 5000 copies of his Wind and Current Charts free of charge on the condition that sailors returned a standardized log of their voyage to the U.S. Naval Observatory. By 1861, he had distributed 200,000 copies free of charge, on the same conditions.[24]
  • 1849 – A network of some 150 volunteer weather observers all over the USA was set up as a part of the Smithsonian Institution's Meteorological Project started by the Smithsonian's first Secretary, Joseph Henry, who used the telegraph to gather volunteers' data and create a large weather map, making new information available to the public daily. For instance, volunteers tracked a tornado passing through Wisconsin and sent the findings via telegraph to the Smithsonian. Henry's project is considered the origin of what later became the National Weather Service. Within a decade, the project had more than 600 volunteer observers and had spread to Canada, Mexico, Latin America, and the Caribbean.[25]
  • 1884 – Publication of the Oxford English Dictionary: 800 volunteers catalogued words to create the first fascicle of the OED.[22]
  • 1916 – Planters Peanuts contest: The Mr. Peanut logo was designed by a 14-year-old boy who won the Planter Peanuts logo contest.[22]
  • 1957 – Jørn Utzon was selected as winner of the design competition for the Sydney Opera House.[22]
  • 1970 – French amateur photo contest C'était Paris en 1970 ("This Was Paris in 1970") was sponsored by the city of Paris, France-Inter radio, and the Fnac: 14,000 photographers produced 70,000 black-and-white prints and 30,000 color slides of the French capital to document the architectural changes of Paris. Photographs were donated to the Bibliothèque historique de la ville de Paris.[26]
  • 1979 – Robert Axelrod invited academics on-line to submit FORTRAN algorithms to play the repeated Prisoner's Dilemma; A tit for tat algorithm ended up in first place.[27]
  • 1981 - Jilly Cooper gathered stories about mongrels for the book Intelligent and Loyal, by putting an advert in newspapers asking people to share stories about their pets for the book.[28][29]
  • 1983 – Richard Stallman began work on the GNU operating system. Programmers fromaround the world contribute to the GNU operating system. Linux kernel is one of the kernels used in this operating system, thus forming the GNU/Linux operating system, which many people call as Linux.
  • 1996 – The Hollywood Stock Exchange was founded: It allowed buying and selling of shares.[22]
  • 1997 – British rock band Marillion raised $60,000 from their fans to help finance their U.S. tour.[22]
  • 1999 – SETI@home was launched by the University of California, Berkeley. Volunteers can contribute to searching for signals that might come from extraterrestrial intelligence by installing a program that uses idle computer time for analyzing chunks of data recorded by radio telescopes involved in the SERENDIP program.[30]
  • 1999– The U.S. Geological Survey's (USGS's) "Did You Feel It?" website was used in the US as a method where by residents could report any tremors or shocks they felt from a recent earthquake and the approximate magnitude of the earthquake.[31]
  • 2000 – JustGiving was established: This online platform allows the public to help raise money for charities.[22]
  • 2000 – UNV Online Volunteering service launched: Connecting people who commit their time and skills over the Internet to help organizations address development challenges.[32]
  • 2000 – iStockPhoto was founded: The free stock imagery website allows the public to contribute to and receive commission for their contributions.[33]
  • 2001 – Launch of Wikipedia: "Free-access, free content Internet encyclopedia".[34]
  • 2001 – Foundation of Topcoder – crowdsourcing software development company.[35][36]
  • 2004 – OpenStreetMap, a collaborative project to create a free editable map of the world, was launched.[37][38]
  • 2004 – Toyota's first "Dream car art" contest: Children were asked globally to draw their "dream car of the future".[39]
  • 2005 – Kodak's "Go for the Gold" contest: Kodak asked anyone to submit a picture of a personal victory.[39]
  • 2005 – Amazon Mechanical Turk (MTurk) was launched publicly on November 2, 2005. It enables businesses to hire remotely located "crowdworkers" to perform discrete on-demand tasks that computers are currently unable to do.[40]
  • 2005 – Reddit was launched in 2005.[41] Reddit is a social media platform and online community where users can submit, discuss and vote, leading to diverse discussions and interactions.
  • 2009 – Waze (then named FreeMap Israel), a community-oriented GPS app, was created.[42] It allows users to submit road information and route data based on location, such as reports of car accidents or traffic, and integrates that data into its routing algorithms for all users of the app.
  • 2010 - Following the Deepwater Horizon oil spill, BP initiated a crowdsourcing effort called the "Deepwater Horizon Response," inviting external experts and the public to submit innovative ideas and technical solutions for containing and cleaning up the massive oil spill. This initiative aimed to leverage collective intelligence to address the unprecedented environmental disaster.[43]
  • 2010 – The 1947 Partition Archive, an oral history project that asked community members around the world to document oral histories from aging witnesses of a significant but under-documented historical event, the 1947 Partition of India, was founded.[44]
  • 2011 – Casting of Flavours (Do us a flavor in the USA) – a campaign launched by PepsiCo's Lay's in Spain. The campaign was to create a new flavor for the snack where the consumers were directly involved in its formation.[45]
  • 2012 - Open Food Facts, a collaborative project to create a libre encyclopedia of food products in the world using smartphones, is launched, followed by extensions on cosmetics, pet food, other products and prices.

Early competitions

[edit]

Crowdsourcing has often been used in the past as a competition to discover a solution. The French government proposed several of these competitions, often rewarded with Montyon Prizes.[46] These included the Leblanc process, or the Alkali prize, where a reward was provided for separating the salt from the alkali, and the Fourneyron's turbine, when the first hydraulic commercial turbine was developed.[47]

In response to a challenge from the French government, Nicolas Appert won a prize for inventing a new way of food preservation that involved sealing food in air-tight jars.[48] The British government provided a similar reward to find an easy way to determine a ship's longitude in the Longitude Prize. During the Great Depression, out-of-work clerks tabulated higher mathematical functions in the Mathematical Tables Project as an outreach project.[49][unreliable source?] One of the largest crowdsourcing campaigns was a public design contest in 2010 hosted by the Indian government's finance ministry to create a symbol for the Indian rupee. Thousands of people sent in entries before the government zeroed in on the final symbol based on the Devanagari script using the letter Ra.[50]

Applications

[edit]

A number of motivations exist for businesses to use crowdsourcing to accomplish their tasks. These include the ability to offload peak demand, access cheap labor and information, generate better results, access a wider array of talent than what is present in one organization, and undertake problems that would have been too difficult to solve internally.[51] Crowdsourcing allows businesses to submit problems on which contributors can work—on topics such as science, manufacturing, biotech, and medicine—optionally with monetary rewards for successful solutions. Although crowdsourcing complicated tasks can be difficult, simple work tasks[specify] can be crowdsourced cheaply and effectively.[52]

Crowdsourcing also has the potential to be a problem-solving mechanism for government and nonprofit use.[53] Urban and transit planning are prime areas for crowdsourcing. For example, from 2008 to 2009, a crowdsourcing project for transit planning in Salt Lake City was created to test the public participation process.[54] Another notable application of crowdsourcing for government problem-solving is Peer-to-Patent, which was an initiative to improve patent quality in the United States through gathering public input in a structured, productive manner.[55]

Researchers have used crowdsourcing systems such as Amazon Mechanical Turk or CloudResearch to aid their research projects by crowdsourcing some aspects of the research process, such as data collection, parsing, and evaluation to the public. Notable examples include using the crowd to create speech and language databases,[56][57] to conduct user studies,[58] and to run behavioral science surveys and experiments.[59] Crowdsourcing systems provided researchers with the ability to gather large amounts of data, and helped researchers to collect data from populations and demographics they may not have access to locally.[60][failed verification]

Artists have also used crowdsourcing systems. In a project called the Sheep Market, Aaron Koblin used Mechanical Turk to collect 10,000 drawings of sheep from contributors around the world.[61] Artist Sam Brown leveraged the crowd by asking visitors of his website explodingdog to send him sentences to use as inspirations for his paintings.[62] Art curator Andrea Grover argues that individuals tend to be more open in crowdsourced projects because they are not being physically judged or scrutinized.[63] As with other types of uses, artists use crowdsourcing systems to generate and collect data. The crowd also can be used to provide inspiration and to collect financial support for an artist's work.[64]

In navigation systems, crowdsourcing from 100 million drivers were used by INRIX to collect users' driving times to provide better GPS routing and real-time traffic updates.[65]

In healthcare

[edit]

The use of crowdsourcing in medical and health research is increasing systematically. The process involves outsourcing tasks or gathering input from a large, diverse groups of people, often facilitated through digital platforms, to contribute to medical research, diagnostics, data analysis, promotion, and various healthcare-related initiatives. Usage of this innovative approach supplies a useful community-based method to improve medical services.

From funding individual medical cases and innovative devices to supporting research, community health initiatives, and crisis responses, crowdsourcing proves its versatile impact in addressing diverse healthcare challenges.[66]

In 2011, UNAIDS initiated the participatory online policy project to better engage young people in decision-making processes related to AIDS.[67] The project acquired data from 3,497 participants across seventy-nine countries through online and offline forums. The outcomes generally emphasized the importance of youth perspectives in shaping strategies to effectively address AIDS which provided a valuable insight for future community empowerment initiatives.

Another approach is sourcing results of clinical algorithms from collective input of participants.[68] Researchers from SPIE developed a crowdsourcing tool, to train individuals, especially middle and high school students in South Korea, to diagnose malaria-infected red blood cells. Using a statistical framework, the platform combined expert diagnoses with those from minimally trained individuals, creating a gold standard library. The objective was to swiftly teach people to achieve great diagnosis accuracy without any prior training.

Cancer medicine journal conducted a review of the studies published between January 2005 and June 2016 on crowdsourcing in cancer research, with the usage PubMed, CINAHL, Scopus, PsychINFO, and Embase.[69] All of them strongly advocate for continuous efforts to refine and expand crowdsourcing applications in academic scholarship. Analysis highlighted the importance of interdisciplinary collaborations and widespread dissemination of knowledge; the review underscored the need to fully harness crowdsourcing's potential to address challenges within cancer research.[69]

In science

[edit]

Astronomy

[edit]

Crowdsourcing in astronomy was used in the early 19th century by astronomer Denison Olmsted. After being awakened in a late November night due to a meteor shower taking place, Olmsted noticed a pattern in the shooting stars. Olmsted wrote a brief report of this meteor shower in the local newspaper. "As the cause of 'Falling Stars' is not understood by meteorologists, it is desirable to collect all the facts attending this phenomenon, stated with as much precision as possible", Olmsted wrote to readers, in a report subsequently picked up and pooled to newspapers nationwide. Responses came pouring in from many states, along with scientists' observations sent to the American Journal of Science and Arts.[70] These responses helped him to make a series of scientific breakthroughs including observing the fact that meteor showers are seen nationwide and fall from space under the influence of gravity. The responses also allowed him to approximate a velocity for the meteors.[71]

A more recent version of crowdsourcing in astronomy is NASA's photo organizing project,[72] which asked internet users to browse photos taken from space and try to identify the location the picture is documenting.[73]

Behavioral science

In the field of behavioral science, crowdsourcing is often used to gather data and insights on human behavior and decision making. Researchers may create online surveys or experiments that are completed by a large number of participants, allowing them to collect a diverse and potentially large amount of data.[59] Crowdsourcing can also be used to gather real-time data on behavior, such as through the use of mobile apps that track and record users' activities and decision making.[74] The use of crowdsourcing in behavioral science has the potential to greatly increase the scope and efficiency of research, and has been used in studies on topics such as psychology research,[75] political attitudes,[76] and social media use.[77]

Energy system research

[edit]

Energy system models require large and diverse datasets, increasingly so given the trend towards greater temporal and spatial resolution.[78] In response, there have been several initiatives to crowdsource this data. Launched in December 2009, OpenEI is a collaborative website run by the US government that provides open energy data.[79][80] While much of its information is from US government sources, the platform also seeks crowdsourced input from around the world.[81] The semantic wiki and database Enipedia also publishes energy systems data using the concept of crowdsourced open information. Enipedia went live in March 2011.[82][83]: 184–188 

Genealogy research

[edit]

Genealogical research used crowdsourcing techniques long before personal computers were common. Beginning in 1942, members of the Church of Jesus Christ of Latter-day Saints encouraged members to submit information about their ancestors. The submitted information was gathered together into a single collection. In 1969, to encourage more participation, the church started the three-generation program. In this program, church members were asked to prepare documented family group record forms for the first three generations. The program was later expanded to encourage members to research at least four generations and became known as the four-generation program.[84]

Institutes that have records of interest to genealogical research have used crowds of volunteers to create catalogs and indices to records.[citation needed]

Genetic genealogy research

Genetic genealogy is a combination of traditional genealogy with genetics. The rise of personal DNA testing, after the turn of the century, by companies such as Gene by Gene, FTDNA, GeneTree, 23andMe, and Ancestry.com, has led to public and semi public databases of DNA testing using crowdsourcing techniques. Citizen science projects have included support, organization, and dissemination of personal DNA (genetic) testing. Similar to amateur astronomy, citizen scientists encouraged by volunteer organizations like the International Society of Genetic Genealogy[85] have provided valuable information and research to the professional scientific community.[86] The Genographic Project, which began in 2005, is a research project carried out by the National Geographic Society's scientific team to reveal patterns of human migration using crowdsourced DNA testing and reporting of results.[87]

Ornithology

[edit]

Another early example of crowdsourcing occurred in the field of ornithology. On 25 December 1900, Frank Chapman, an early officer of the National Audubon Society, initiated a tradition dubbed the "Christmas Day Bird Census". The project called birders from across North America to count and record the number of birds in each species they witnessed on Christmas Day. The project was successful, and the records from 27 different contributors were compiled into one bird census, which tallied around 90 species of birds.[88] This large-scale collection of data constituted an early form of citizen science, the premise upon which crowdsourcing is based. In the 2012 census, more than 70,000 individuals participated across 2,369 bird count circles.[89] Christmas 2014 marked the National Audubon Society's 115th annual Christmas Bird Count.

The European-Mediterranean Seismological Centre (EMSC) has developed a seismic detection system by monitoring the traffic peaks on its website and analyzing keywords used on Twitter.[90]

In journalism

[edit]

Crowdsourcing is increasingly used in professional journalism. Journalists are able to organize crowdsourced information by fact checking the information, and then using the information they have gathered in their articles as they see fit.[citation needed] A daily newspaper in Sweden has successfully used crowdsourcing in investigating the home loan interest rates in the country in 2013–2014, which resulted in over 50,000 submissions.[91] A daily newspaper in Finland crowdsourced an investigation into stock short-selling in 2011–2012, and the crowdsourced information led to revelations of a tax evasion system by a Finnish bank. The bank executive was fired and policy changes followed.[92] TalkingPointsMemo in the United States asked its readers to examine 3,000 emails concerning the firing of federal prosecutors in 2008. The British newspaper The Guardian crowdsourced the examination of hundreds of thousands of documents in 2009.[93]

Data donation

[edit]

Data donation is a crowdsourcing approach to gather digital data. It is used by researchers and organizations to gain access to data from online platforms, websites, search engines and apps and devices. Data donation projects usually rely on participants volunteering their authentic digital profile information. Examples include:

In Social Media

[edit]

Crowdsourcing is used in large scale media, such as the community notes system of the X platform. Crowdsourcing on such platforms is thought to be effective in combating partisan misinformation on social media when certain conditions are met.[101][102] Success may depend on trust in fact-checking sources, the ability to present information that challenges previous beliefs without causing excessive dissonance, and having a sufficiently large and diverse crowd of participants. Effective crowdsourcing interventions must navigate politically polarized environments where trusted sources may be less inclined to provide dissonant opinions. By leveraging network analysis to connect users with neighboring communities outside their ideological echo chambers, crowdsourcing can provide an additional layer of content moderation.

In public policy

[edit]

Crowdsourcing public policy and the production of public services is also referred to as citizen sourcing. While some scholars argue crowdsourcing for this purpose as a policy tool[103] or a definite means of co-production,[104] others question that and argue that crowdsourcing should be considered just as a technological enabler that simply increases speed and ease of participation.[105] Crowdsourcing can also play a role in democratization.[106]

The first conference focusing on Crowdsourcing for Politics and Policy took place at Oxford University, under the auspices of the Oxford Internet Institute in 2014. Research has emerged since 2012[107] which focused on the use of crowdsourcing for policy purposes.[108][109] These include experimentally investigating the use of Virtual Labor Markets for policy assessment,[110] and assessing the potential for citizen involvement in process innovation for public administration.[111]

Governments across the world are increasingly using crowdsourcing for knowledge discovery and civic engagement.[citation needed] Iceland crowdsourced their constitution reform process in 2011, and Finland has crowdsourced several law reform processes to address their off-road traffic laws. The Finnish government allowed citizens to go on an online forum to discuss problems and possible resolutions regarding some off-road traffic laws.[citation needed] The crowdsourced information and resolutions would then be passed on to legislators to refer to when making a decision, allowing citizens to contribute to public policy in a more direct manner.[112][113] Palo Alto crowdsources feedback for its Comprehensive City Plan update in a process started in 2015.[114] The House of Representatives in Brazil has used crowdsourcing in policy-reforms.[115]

NASA used crowdsourcing to analyze large sets of images. As part of the Open Government Initiative of the Obama Administration, the General Services Administration collected and amalgamated suggestions for improving federal websites.[115]

For part of the Obama and Trump Administrations, the We the People system collected signatures on petitions, which were entitled to an official response from the White House once a certain number had been reached. Several U.S. federal agencies ran inducement prize contests, including NASA and the Environmental Protection Agency.[116][115]

[edit]

Crowdsourcing has been used extensively for gathering language-related data.

For dictionary work, crowdsourcing was applied over a hundred years ago by the Oxford English Dictionary editors using paper and postage. It has also been used for collecting examples of proverbs on a specific topic (e.g. religious pluralism) for a printed journal.[117] Crowdsourcing language-related data online has proven very effective and many dictionary compilation projects used crowdsourcing. It is used particularly for specialist topics and languages that are not well documented, such as for the Oromo language.[118] Software programs have been developed for crowdsourced dictionaries, such as WeSay.[119] A slightly different form of crowdsourcing for language data was the online creation of scientific and mathematical terminology for American Sign Language.[120]

In linguistics, crowdsourcing strategies have been applied to estimate word knowledge, vocabulary size, and word origin.[121] Implicit crowdsourcing on social media has also approximating sociolinguistic data efficiently. Reddit conversations in various location-based subreddits were analyzed for the presence of grammatical forms unique to a regional dialect. These were then used to map the extent of the speaker population. The results could roughly approximate large-scale surveys on the subject without engaging in field interviews.[122]

Mining publicly available social media conversations can be used as a form of implicit crowdsourcing to approximate the geographic extent of speaker dialects.[122] Proverb collection is also being done via crowdsourcing on the Web, most notably for the Pashto language of Afghanistan and Pakistan.[123][124][125] Crowdsourcing has been extensively used to collect high-quality gold standards for creating automatic systems in natural language processing (e.g. named entity recognition, entity linking).[126]

In product design

[edit]

Organizations often leverage crowdsourcing to gather ideas for new products as well as for the refinement of established product.[43] Lego allows users to work on new product designs while conducting requirements testing. Any user can provide a design for a product, and other users can vote on the product. Once the submitted product has received 10,000 votes, it will be formally reviewed in stages and go into production with no impediments such as legal flaws identified. The creator receives royalties from the net income.[127] Labelling new products as "customer-ideated" through crowdsourcing initiatives, as opposed to not specifying the source of design, leads to a substantial increase in the actual market performance of the products. Merely highlighting the source of design to customers, particularly, attributing the product to crowdsourcing efforts from user communities, can lead to a significant boost in product sales. Consumers perceive "customer-ideated" products as more effective in addressing their needs, leading to a quality inference. The design mode associated with crowdsourced ideas is considered superior in generating promising new products, contributing to the observed increase in market performance.[128]

In business

[edit]

Crowdsourcing is widely used by businesses to source feedback and suggestions on how to improve their products and services.[43] Homeowners can use Airbnb to list their accommodation or unused rooms. Owners set their own nightly, weekly and monthly rates and accommodations. The business, in turn, charges guests and hosts a fee. Guests usually end up spending between $9 and $15.[129] They have to pay a booking fee every time they book a room. The landlord, in turn, pays a service fee for the amount due. The company has 1,500 properties in 34,000 cities in more than 190 countries.[citation needed]

In market research

[edit]

Crowdsourcing is frequently used in market research as a way to gather insights and opinions from a large number of consumers.[130] Companies may create online surveys or focus groups that are open to the general public, allowing them to gather a diverse range of perspectives on their products or services. This can be especially useful for companies seeking to understand the needs and preferences of a particular market segment or to gather feedback on the effectiveness of their marketing efforts. The use of crowdsourcing in market research allows companies to quickly and efficiently gather a large amount of data and insights that can inform their business decisions.[131]

Other examples

[edit]
  • GeographyVolunteered geographic information (VGI) is geographic information generated through crowdsourcing, as opposed to traditional methods of Professional Geographic Information (PGI).[132] In describing the built environment, VGI has many advantages over PGI, primarily perceived currency,[133] accuracy[134] and authority.[135] OpenStreetMap is an example of crowdsourced mapping project.[38][37]
  • Engineering — Many companies are introducing crowdsourcing to grow their engineering capabilities and find solutions to unsolved technical challenges and the need to adopt newest technologies such as 3D printing and the IOT.[citation needed]
  • Libraries, museums and archives — Newspaper text correction at the National Library of Australia was an early, influential example of work with text transcriptions for crowdsourcing in cultural heritage institutions.[136] The Steve Museum project provided a prototype for categorizing artworks.[137] Crowdsourcing is used in libraries for OCR corrections on digitized texts, for tagging and for funding, especially in the absence of financial and human means. Volunteers can contribute explicitly with conscious effort or implicitly without being known by turning the text on the raw newspaper image into human corrected digital form.[138]
  • Agriculture — Crowdsource research also applies to the field of agriculture. Crowdsourcing can be used to help farmers and experts to dentify different types of weeds[139] from the fields and also to provide assistance in removing the weeds.
  • Cheating in bridgeBoye Brogeland initiated a crowdsourcing investigation of cheating by top-level bridge players that showed several players as guilty, which led to their suspension.[140]
  • Open-source software and Crowdsourcing software development have been used extensively in the domain of software development.
  • Healthcare — Research has emerged that outlined the use of crowdsourcing techniques in the public health domain.[141][142][143] The collective intelligence outcomes from crowdsourcing are being generated in three broad categories of public health care: health promotion,[142] health research,[144] and health maintenance.[145] Crowdsourcing also enables researchers to move from small homogeneous groups of participants to large heterogenous groups[146] beyond convenience samples such as students or higher educated people. The SESH group focuses on using crowdsourcing to improve health.

Methods

[edit]

Internet and digital technologies have massively expanded the opportunities for crowdsourcing. However, the effect of user communication and platform presentation can have a major bearing on the success of an online crowdsourcing project.[19] The crowdsourced problem can range from huge tasks (such as finding alien life or mapping earthquake zones) or very small (identifying images). Some examples of successful crowdsourcing themes are problems that bug people, things that make people feel good about themselves, projects that tap into niche knowledge of proud experts, and subjects that people find sympathetic.[147]

Crowdsourcing can either take an explicit or an implicit route:

  • Explicit crowdsourcing lets users work together to evaluate, share, and build different specific tasks, while implicit crowdsourcing means that users solve a problem as a side effect of something else they are doing. With explicit crowdsourcing, users can evaluate particular items like books or webpages, or share by posting products or items. Users can also build artifacts by providing information and editing other people's work.[citation needed]
  • Implicit crowdsourcing can take two forms: standalone and piggyback. Standalone allows people to solve problems as a side effect of the task they are actually doing, whereas piggyback takes users' information from a third-party website to gather information.[148] This is also known as data donation.

In his 2013 book, Crowdsourcing, Daren C. Brabham puts forth a problem-based typology of crowdsourcing approaches:[149]

  • Knowledge discovery and management is used for information management problems where an organization mobilizes a crowd to find and assemble information. It is ideal for creating collective resources.
  • Distributed human intelligence tasking (HIT) is used for information management problems where an organization has a set of information in hand and mobilizes a crowd to process or analyze the information. It is ideal for processing large data sets that computers cannot easily do. Amazon Mechanical Turk uses this approach.
  • Broadcast search is used for ideation problems where an organization mobilizes a crowd to come up with a solution to a problem that has an objective, provable right answer. It is ideal for scientific problem-solving.
  • Peer-vetted creative production is used for ideation problems, where an organization mobilizes a crowd to come up with a solution to a problem which has an answer that is subjective or dependent on public support. It is ideal for design, aesthetic, or policy problems.

Ivo Blohm identifies four types of Crowdsourcing Platforms: Microtasking, Information Pooling, Broadcast Search, and Open Collaboration. They differ in the diversity and aggregation of contributions that are created. The diversity of information collected can either be homogenous or heterogenous. The aggregation of information can either be selective or integrative.[definition needed][150] Some common categories of crowdsourcing have been used effectively in the commercial world include crowdvoting, crowdsolving, crowdfunding, microwork, creative crowdsourcing, crowdsource workforce management, and inducement prize contests.[151]

In their conceptual review of the crowdsourcing, Linus Dahlander, Lars Bo Jeppesen, and Henning Piezunka distinguish four steps in the crowdsourcing process: Define, Broadcast, Attract, and Select.[152]

Crowdvoting

[edit]

Crowdvoting occurs when a website gathers a large group's opinions and judgments on a certain topic. Some crowdsourcing tools and platforms allow participants to rank each other's contributions, e.g. in answer to the question "What is one thing we can do to make Acme a great company?" One common method for ranking is "like" counting, where the contribution with the most "like" votes ranks first. This method is simple and easy to understand, but it privileges early contributions, which have more time to accumulate votes.[citation needed] In recent years, several crowdsourcing companies have begun to use pairwise comparisons backed by ranking algorithms. Ranking algorithms do not penalize late contributions.[citation needed] They also produce results quicker. Ranking algorithms have proven to be at least 10 times faster than manual stack ranking.[153] One drawback, however, is that ranking algorithms are more difficult to understand than vote counting.

The Iowa Electronic Market is a prediction market that gathers crowds' views on politics and tries to ensure accuracy by having participants pay money to buy and sell contracts based on political outcomes.[154] Some of the most famous examples have made use of social media channels: Domino's Pizza, Coca-Cola, Heineken, and Sam Adams have crowdsourced a new pizza, bottle design, beer, and song respectively.[155] A website called Threadless selected the T-shirts it sold by having users provide designs and vote on the ones they like, which are then printed and available for purchase.[18]

The California Report Card (CRC), a program jointly launched in January 2014 by the Center for Information Technology Research in the Interest of Society[156] and Lt. Governor Gavin Newsom, is an example of modern-day crowd voting. Participants access the CRC online and vote on six timely issues. Through principal component analysis, the users are then placed into an online "café" in which they can present their own political opinions and grade the suggestions of other participants. This system aims to effectively involve the greater public in relevant political discussions and highlight the specific topics with which people are most concerned.

Crowdvoting's value in the movie industry was shown when in 2009 a crowd accurately predicted the success or failure of a movie based on its trailer,[157][158] a feat that was replicated in 2013 by Google.[159]

On Reddit, users collectively rate web content, discussions and comments as well as questions posed to persons of interest in "AMA" and AskScience online interviews.[cleanup needed]

In 2017, Project Fanchise purchased a team in the Indoor Football League and created the Salt Lake Screaming Eagles, a fan run team. Using a mobile app, the fans voted on the day-to-day operations of the team, the mascot name, signing of players and even offensive play calling during games.[160]

Crowdfunding

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Crowdfunding is the process of funding projects by a multitude of people contributing a small amount to attain a certain monetary goal, typically via the Internet.[161] Crowdfunding has been used for both commercial and charitable purposes.[162] The crowdfuding model that has been around the longest is rewards-based crowdfunding. This model is where people can prepurchase products, buy experiences, or simply donate. While this funding may in some cases go towards helping a business, funders are not allowed to invest and become shareholders via rewards-based crowdfunding.[163]

Individuals, businesses, and entrepreneurs can showcase their businesses and projects by creating a profile, which typically includes a short video introducing their project, a list of rewards per donation, and illustrations through images.[citation needed] Funders make monetary contribution for numerous reasons:

  1. They connect to the greater purpose of the campaign, such as being a part of an entrepreneurial community and supporting an innovative idea or product.[164]
  2. They connect to a physical aspect of the campaign like rewards and gains from investment.[164]
  3. They connect to the creative display of the campaign's presentation.
  4. They want to see new products before the public.[164]

The dilemma for equity crowdfunding in the US as of 2012 was during a refinement process for the regulations of the Securities and Exchange Commission, which had until 1 January 2013 to tweak the fundraising methods. The regulators were overwhelmed trying to regulate Dodd-Frank and all the other rules and regulations involving public companies and the way they traded. Advocates of regulation claimed that crowdfunding would open up the flood gates for fraud, called it the "wild west" of fundraising, and compared it to the 1980s days of penny stock "cold-call cowboys". The process allowed for up to $1 million to be raised without some of the regulations being involved. Companies under the then-current proposal would have exemptions available and be able to raise capital from a larger pool of persons, which can include lower thresholds for investor criteria, whereas the old rules required that the person be an "accredited" investor. These people are often recruited from social networks, where the funds can be acquired from an equity purchase, loan, donation, or ordering. The amounts collected have become quite high, with requests that are over a million dollars for software such as Trampoline Systems, which used it to finance the commercialization of their new software.[citation needed]

Inducement prize contests

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Web-based idea competitions or inducement prize contests often consist of generic ideas, cash prizes, and an Internet-based platform to facilitate easy idea generation and discussion. An example of these competitions includes an event like IBM's 2006 "Innovation Jam", attended by over 140,000 international participants and yielded around 46,000 ideas.[165][166] Another example is the Netflix Prize in 2009. People were asked to come up with a recommendation algorithm that is more accurate than Netflix's current algorithm. It had a grand prize of US$1,000,000, and it was given to a team which designed an algorithm that beat Netflix's own algorithm for predicting ratings by 10.06%.[citation needed]

Another example of competition-based crowdsourcing is the 2009 DARPA balloon experiment, where DARPA placed 10 balloon markers across the United States and challenged teams to compete to be the first to report the location of all the balloons. A collaboration of efforts was required to complete the challenge quickly and in addition to the competitive motivation of the contest as a whole, the winning team (MIT, in less than nine hours) established its own "collaborapetitive" environment to generate participation in their team.[167] A similar challenge was the Tag Challenge, funded by the US State Department, which required locating and photographing individuals in five cities in the US and Europe within 12 hours based only on a single photograph. The winning team managed to locate three suspects by mobilizing volunteers worldwide using a similar incentive scheme to the one used in the balloon challenge.[168]

Using open innovation platforms is an effective way to crowdsource people's thoughts and ideas for research and development. The company InnoCentive is a crowdsourcing platform for corporate research and development where difficult scientific problems are posted for crowds of solvers to discover the answer and win a cash prize that ranges from $10,000 to $100,000 per challenge.[18] InnoCentive, of Waltham, Massachusetts, and London, England, provides access to millions of scientific and technical experts from around the world. The company claims a success rate of 50% in providing successful solutions to previously unsolved scientific and technical problems. The X Prize Foundation creates and runs incentive competitions offering between $1 million and $30 million for solving challenges. Local Motors is another example of crowdsourcing, and it is a community of 20,000 automotive engineers, designers, and enthusiasts that compete to build off-road rally trucks.[169]

Implicit crowdsourcing

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Implicit crowdsourcing is less obvious because users do not necessarily know they are contributing, yet can still be very effective in completing certain tasks.[citation needed] Rather than users actively participating in solving a problem or providing information, implicit crowdsourcing involves users doing another task entirely where a third party gains information for another topic based on the user's actions.[18]

A good example of implicit crowdsourcing is the ESP game, where users find words to describe Google images, which are then used as metadata for the images. Another popular use of implicit crowdsourcing is through reCAPTCHA, which asks people to solve CAPTCHAs to prove they are human, and then provides CAPTCHAs from old books that cannot be deciphered by computers, to digitize them for the web. Like many tasks solved using the Mechanical Turk, CAPTCHAs are simple for humans, but often very difficult for computers.[148]

Piggyback crowdsourcing can be seen most frequently by websites such as Google that data-mine a user's search history and websites to discover keywords for ads, spelling corrections, and finding synonyms. In this way, users are unintentionally helping to modify existing systems, such as Google Ads.[58]

Other types

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  • Creative crowdsourcing involves sourcing people for creative projects such as graphic design, crowdsourcing architecture, product design,[12] apparel design, movies,[170] writing, company naming,[171] illustration, etc.[172][173] While crowdsourcing competitions have been used for decades in some creative fields such as architecture, creative crowdsourcing has proliferated with the recent development of web-based platforms where clients can solicit a wide variety of creative work at lower cost than by traditional means.[citation needed]
  • Crowdshipping (crowd-shipping) is a peer-to-peer shipping service, usually conducted via an online platform or marketplace.[174] There are several methods that have been categorized as crowd-shipping:
    • Travelers heading in the direction of the buyer, and are willing to bring the package as part of their luggage for a reward.[175]
    • Truck drivers whose route lies along the buyer's location and who are willing to take extra items in their truck.[176]
    • Community-based platforms that connect international buyers and local forwarders, by allowing buyers to use forwarder's address as purchase destination, after which forwarders ship items further to the buyer.[177]
  • Crowdsolving is a collaborative and holistic way of solving a problem through many people, communities, groups, or resources. It is a type of crowdsourcing with focus on complex and intellectually demanding problems requiring considerable effort, and the quality or uniqueness of contribution.[178]
    • Problem–idea chains are a form of idea crowdsourcing and crowdsolving, where individuals are asked to submit ideas to solve problems and then problems that can be solved with those ideas. The aim is to find encourage individuals to find practical solutions to problems that are well thought through.[179]
  • Macrowork tasks typically have these characteristics: they can be done independently, they take a fixed amount of time, and they require special skills. Macro-tasks could be part of specialized projects or could be part of a large, visible project where workers pitch in wherever they have the required skills. The key distinguishing factors are that macro-work requires specialized skills and typically takes longer, while microwork requires no specialized skills.
  • Microwork is a crowdsourcing platform that allows users to do small tasks for which computers lack aptitude in for low amounts of money. Amazon's Mechanical Turk has created many different projects for users to participate in, where each task requires very little time and offers a very small amount in payment.[15] When choosing tasks, since only certain users "win", users learn to submit later and pick less popular tasks to increase the likelihood of getting their work chosen.[180] An example of a Mechanical Turk project is when users searched satellite images for a boat to find Jim Gray, a missing computer scientist.[148]
  • Mobile crowdsourcing involves activities that take place on smartphones or mobile platforms that are frequently characterized by GPS technology.[181] This allows for real-time data gathering and gives projects greater reach and accessibility. However, mobile crowdsourcing can lead to an urban bias, and can have safety and privacy concerns.[182][183][184]
  • Simple projects are those that require a large amount of time and skills compared to micro and macro-work. While an example of macro-work would be writing survey feedback, simple projects rather include activities like writing a basic line of code or programming a database, which both require a larger time commitment and skill level. These projects are usually not found on sites like Amazon Mechanical Turk, and are rather posted on platforms like Upwork that call for a specific expertise.[185]
  • Complex projects generally take the most time, have higher stakes, and call for people with very specific skills. These are generally "one-off" projects that are difficult to accomplish and can include projects such as designing a new product that a company hopes to patent. Such projects are considered to be complex because design is a meticulous process that requires a large amount of time to perfect, and people completing the project must have specialized training in design to effectively complete the project. These projects usually pay the highest, yet are rarely offered.[186]
  • Crowdsourcing-Based Optimization refers to a class of methods that utilize crowdsourcing to enable a group of workers to collaboratively collect data and solve optimization problems related to the data. Due to the heterogeneity of workers, the data collected varies, and the workers' understanding of the optimization problems also differs, thus posing challenges to collaboratively solve a global optimization problem. Representative methods for solving Crowdsourcing-Based Optimization include CrowdEC, which is a mechanism that dispatch the optimization tasks to a group of workers that collaborate to perform evolutionary computation (EC) in a distributed manner.[187]

Demographics of the crowd

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The crowd is an umbrella term for the people who contribute to crowdsourcing efforts. Though it is sometimes difficult to gather data about the demographics of the crowd as a whole, several studies have examined various specific online platforms. Amazon Mechanical Turk has received a great deal of attention in particular. A study in 2008 by Ipeirotis found that users at that time were primarily American, young, female, and well-educated, with 40% earning more than $40,000 per year. In November 2009, Ross found a very different Mechanical Turk population where 36% of which was Indian. Two-thirds of Indian workers were male, and 66% had at least a bachelor's degree. Two-thirds had annual incomes less than $10,000, with 27% sometimes or always depending on income from Mechanical Turk to make ends meet.[188] More recent studies have found that U.S. Mechanical Turk workers are approximately 58% female, and nearly 67% of workers are in their 20s and 30s.[59][189][190][191] Close to 80% are White, and 9% are Black. MTurk workers are less likely to be married or have children as compared to the general population. In the US population over 18, 45% are unmarried, while the proportion of unmarried workers on MTurk is around 57%. Additionally, about 55% of MTurk workers do not have any children, which is significantly higher than the general population. Approximately 68% of U.S. workers are employed, compared to 60% in the general population. MTurk workers in the U.S. are also more likely to have a four-year college degree (35%) compared to the general population (27%). Politics within the U.S. sample of MTurk are skewed liberal, with 46% Democrats, 28% Republicans, and 26%  "other". MTurk workers are also less religious than the U.S. population, with 41% religious, 20% spiritual, 21% agnostic, and 16% atheist.

The demographics of Microworkers.com differ from Mechanical Turk in that the US and India together accounting for only 25% of workers; 197 countries are represented among users, with Indonesia (18%) and Bangladesh (17%) contributing the largest share. However, 28% of employers are from the US.[192]

Another study of the demographics of the crowd at iStockphoto found a crowd that was largely white, middle- to upper-class, higher educated, worked in a so-called "white-collar job" and had a high-speed Internet connection at home.[193] In a crowd-sourcing diary study of 30 days in Europe, the participants were predominantly higher educated women.[146]

Studies have also found that crowds are not simply collections of amateurs or hobbyists. Rather, crowds are often professionally trained in a discipline relevant to a given crowdsourcing task and sometimes hold advanced degrees and many years of experience in the profession.[193][194][195][196] Claiming that crowds are amateurs, rather than professionals, is both factually untrue and may lead to marginalization of crowd labor rights.[197]

Gregory Saxton et al. studied the role of community users, among other elements, during his content analysis of 103 crowdsourcing organizations. They developed a taxonomy of nine crowdsourcing models (intermediary model, citizen media production, collaborative software development, digital goods sales, product design, peer-to-peer social financing, consumer report model, knowledge base building model, and collaborative science project model) in which to categorize the roles of community users, such as researcher, engineer, programmer, journalist, graphic designer, etc., and the products and services developed.[198]

Motivations

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Contributors

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Many researchers suggest that both intrinsic and extrinsic motivations cause people to contribute to crowdsourced tasks and these factors influence different types of contributors.[113][193][194][196][199][200][201][202][203] For example, people employed in a full-time position rate human capital advancement as less important than part-time workers do, while women rate social contact as more important than men do.[200]

Intrinsic motivations are broken down into two categories: enjoyment-based and community-based motivations. Enjoyment-based motivations refer to motivations related to the fun and enjoyment contributors experience through their participation. These motivations include: skill variety, task identity, task autonomy, direct feedback from the job, and taking the job as a pastime.[citation needed] Community-based motivations refer to motivations related to community participation, and include community identification and social contact. In crowdsourced journalism, the motivation factors are intrinsic: the crowd is driven by a possibility to make social impact, contribute to social change, and help their peers.[199]

Extrinsic motivations are broken down into three categories: immediate payoffs, delayed payoffs, and social motivations. Immediate payoffs, through monetary payment, are the immediately received compensations given to those who complete tasks. Delayed payoffs are benefits that can be used to generate future advantages, such as training skills and being noticed by potential employers. Social motivations are the rewards of behaving pro-socially,[204] such as the altruistic motivations of online volunteers. Chandler and Kapelner found that US users of the Amazon Mechanical Turk were more likely to complete a task when told they were going to help researchers identify tumor cells, than when they were not told the purpose of their task. However, of those who completed the task, quality of output did not depend on the framing.[205]

Motivation in crowdsourcing is often a mix of intrinsic and extrinsic factors.[206] In a crowdsourced law-making project, the crowd was motivated by both intrinsic and extrinsic factors. Intrinsic motivations included fulfilling civic duty, affecting the law for sociotropic reasons, to deliberate with and learn from peers. Extrinsic motivations included changing the law for financial gain or other benefits. Participation in crowdsourced policy-making was an act of grassroots advocacy, whether to pursue one's own interest or more altruistic goals, such as protecting nature.[113] Participants in online research studies report their motivation as both intrinsic enjoyment and monetary gain.[207][208][190]

Another form of social motivation is prestige or status. The International Children's Digital Library recruited volunteers to translate and review books. Because all translators receive public acknowledgment for their contributions, Kaufman and Schulz cite this as a reputation-based strategy to motivate individuals who want to be associated with institutions that have prestige. The Mechanical Turk uses reputation as a motivator in a different sense, as a form of quality control. Crowdworkers who frequently complete tasks in ways judged to be inadequate can be denied access to future tasks, whereas workers who pay close attention may be rewarded by gaining access to higher-paying tasks or being on an "Approved List" of workers. This system may incentivize higher-quality work.[209] However, this system only works when requesters reject bad work, which many do not.[210]

Despite the potential global reach of IT applications online, recent research illustrates that differences in location[which?] affect participation outcomes in IT-mediated crowds.[211]

Limitations and controversies

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While there it lots of anecdotal evidence that illustrates the potential of crowdsourcing and the benefits that organizations have derived, there is scientific evidence that crowdsourcing initiatives often fail.[212] At least six major topics cover the limitations and controversies about crowdsourcing:

  1. Failure to attract contributions
  2. Impact of crowdsourcing on product quality
  3. Entrepreneurs contribute less capital themselves
  4. Increased number of funded ideas
  5. The value and impact of the work received from the crowd
  6. The ethical implications of low wages paid to workers
  7. Trustworthiness and informed decision making

Failure to attract contributions

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Crowdsourcing initiatives often fail to attract sufficient or beneficial contributions. The vast majority of crowdsourcing initiatives hardly attract contributions; an analysis of thousands of organizations' crowdsourcing initiatives illustrates that only the 90th percentile of initiatives attracts more than one contribution a month.[203] While crowdsourcing initiatives may be effective in isolation, when faced with competition they mail fail to attract sufficient contributions. Nagaraj and Piezunka (2024) illustrate that OpenStreetMap struggled to attract contributions once Google Maps entered a country.

Impact of crowdsourcing on product quality

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Crowdsourcing allows anyone to participate, allowing for many unqualified participants and resulting in large quantities of unusable contributions.[213] Companies, or additional crowdworkers, then have to sort through the low-quality contributions. The task of sorting through crowdworkers' contributions, along with the necessary job of managing the crowd, requires companies to hire actual employees, thereby increasing management overhead.[214] For example, susceptibility to faulty results can be caused by targeted, malicious work efforts. Since crowdworkers completing microtasks are paid per task, a financial incentive often causes workers to complete tasks quickly rather than well.[59] Verifying responses is time-consuming, so employers often depend on having multiple workers complete the same task to correct errors. However, having each task completed multiple times increases time and monetary costs.[215] Some companies, like CloudResearch, control data quality by repeatedly vetting crowdworkers to ensure they are paying attention and providing high-quality work.[210]

Crowdsourcing quality is also impacted by task design. Lukyanenko et al.[216] argue that, the prevailing practice of modeling crowdsourcing data collection tasks in terms of fixed classes (options), unnecessarily restricts quality. Results demonstrate that information accuracy depends on the classes used to model domains, with participants providing more accurate information when classifying phenomena at a more general level (which is typically less useful to sponsor organizations, hence less common).[clarification needed] Further, greater overall accuracy is expected when participants could provide free-form data compared to tasks in which they select from constrained choices. In behavioral science research, it is often recommended to include open-ended responses, in addition to other forms of attention checks, to assess data quality.[217][218]

Just as limiting, oftentimes there is not enough skills or expertise in the crowd to successfully accomplish the desired task. While this scenario does not affect "simple" tasks such as image labeling, it is particularly problematic for more complex tasks, such as engineering design or product validation. A comparison between the evaluation of business models from experts and an anonymous online crowd showed that an anonymous online crowd cannot evaluate business models to the same level as experts.[219] In these cases, it may be difficult or even impossible to find qualified people in the crowd, as their responses represent only a small fraction of the workers compared to consistent, but incorrect crowd members.[220] However, if the task is "intermediate" in its difficulty, estimating crowdworkers' skills and intentions and leveraging them for inferring true responses works well,[221] albeit with an additional computation cost.[citation needed]

Crowdworkers are a nonrandom sample of the population. Many researchers use crowdsourcing to quickly and cheaply conduct studies with larger sample sizes than would be otherwise achievable. However, due to limited access to the Internet, participation in low developed countries is relatively low. Participation in highly developed countries is similarly low, largely because the low amount of pay is not a strong motivation for most users in these countries. These factors lead to a bias in the population pool towards users in medium developed countries, as deemed by the human development index.[222] Participants in these countries sometimes masquerade as U.S. participants to gain access to certain tasks. This led to the "bot scare" on Amazon Mechanical Turk in 2018, when researchers thought bots were completing research surveys due to the lower quality of responses originating from medium-developed countries.[218][223]

The likelihood that a crowdsourced project will fail due to lack of monetary motivation or too few participants increases over the course of the project. Tasks that are not completed quickly may be forgotten, buried by filters and search procedures. This results in a long-tail power law distribution of completion times.[224] Additionally, low-paying research studies online have higher rates of attrition, with participants not completing the study once started.[60] Even when tasks are completed, crowdsourcing does not always produce quality results. When Facebook began its localization program in 2008, it encountered some criticism for the low quality of its crowdsourced translations.[225] One of the problems of crowdsourcing products is the lack of interaction between the crowd and the client. Usually little information is known about the final product, and workers rarely interacts with the final client in the process. This can decrease the quality of product as client interaction is considered to be a vital part of the design process.[226]

An additional cause of the decrease in product quality that can result from crowdsourcing is the lack of collaboration tools. In a typical workplace, coworkers are organized in such a way that they can work together and build upon each other's knowledge and ideas. Furthermore, the company often provides employees with the necessary information, procedures, and tools to fulfill their responsibilities. However, in crowdsourcing, crowd-workers are left to depend on their own knowledge and means to complete tasks.[214]

A crowdsourced project is usually expected to be unbiased by incorporating a large population of participants with a diverse background. However, most of the crowdsourcing works are done by people who are paid or directly benefit from the outcome (e.g. most of open source projects working on Linux). In many other cases, the end product is the outcome of a single person's endeavor, who creates the majority of the product, while the crowd only participates in minor details.[227]

Entrepreneurs contribute less capital themselves

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To make an idea turn into a reality, the first component needed is capital. Depending on the scope and complexity of the crowdsourced project, the amount of necessary capital can range from a few thousand dollars to hundreds of thousands, if not more. The capital-raising process can take from days to months depending on different variables, including the entrepreneur's network and the amount of initial self-generated capital.[citation needed]

The crowdsourcing process allows entrepreneurs to access a wide range of investors who can take different stakes in the project.[228] As an effect, crowdsourcing simplifies the capital-raising process and allows entrepreneurs to spend more time on the project itself and reaching milestones rather than dedicating time to get it started. Overall, the simplified access to capital can save time to start projects and potentially increase the efficiency of projects.[citation needed]

Others argue that easier access to capital through a large number of smaller investors can hurt the project and its creators. With a simplified capital-raising process involving more investors with smaller stakes, investors are more risk-seeking because they can take on an investment size with which they are comfortable.[228] This leads to entrepreneurs losing possible experience convincing investors who are wary of potential risks in investing because they do not depend on one single investor for the survival of their project. Instead of being forced to assess risks and convince large institutional investors on why their project can be successful, wary investors can be replaced by others who are willing to take on the risk.

Some translation companies and translation tool consumers pretend to use crowdsourcing as a means for drastically cutting costs, instead of hiring professional translators. This situation has been systematically denounced by IAPTI and other translator organizations.[229]

Increased number of funded ideas

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The raw number of ideas that get funded and the quality of the ideas is a large controversy over the issue of crowdsourcing.

Proponents argue that crowdsourcing is beneficial because it allows the formation of startups with niche ideas that would not survive venture capitalist or angel funding, which are oftentimes the primary investors in startups. Many ideas are scrapped in their infancy due to insufficient support and lack of capital, but crowdsourcing allows these ideas to be started if an entrepreneur can find a community to take interest in the project.[230]

Crowdsourcing allows those who would benefit from the project to fund and become a part of it, which is one way for small niche ideas get started.[231] However, when the number of projects grows, the number of failures also increases. Crowdsourcing assists the development of niche and high-risk projects due to a perceived need from a select few who seek the product. With high risk and small target markets, the pool of crowdsourced projects faces a greater possible loss of capital, lower return, and lower levels of success.[232]

[edit]

Because crowdworkers are considered independent contractors rather than employees, they are not guaranteed minimum wage. In practice, workers using Amazon Mechanical Turk generally earn less than minimum wage. In 2009, it was reported that United States Turk users earned an average of $2.30 per hour for tasks, while users in India earned an average of $1.58 per hour, which is below minimum wage in the United States (but not in India).[188][233] In 2018, a survey of 2,676 Amazon Mechanical Turk workers doing 3.8 million tasks found that the median hourly wage was approximately $2 per hour, and only 4% of workers earned more than the federal minimum wage of $7.25 per hour.[234] Some researchers who have considered using Mechanical Turk to get participants for research studies have argued that the wage conditions might be unethical.[60][235] However, according to other research, workers on Amazon Mechanical Turk do not feel they are exploited and are ready to participate in crowdsourcing activities in the future.[236] A more recent study using stratified random sampling to access a representative sample of Mechanical Turk workers found that the U.S. MTurk population is financially similar to the general population.[190] Workers tend to participate in tasks as a form of paid leisure and to supplement their primary income, and only 7% view it as a full-time job. Overall, workers rated MTurk as less stressful than other jobs. Workers also earn more than previously reported, about $6.50 per hour. They see MTurk as part of the solution to their financial situation and report rare upsetting experiences. They also perceive requesters on MTurk as fairer and more honest than employers outside of the platform.[190]

When Facebook began its localization program in 2008, it received criticism for using free labor in crowdsourcing the translation of site guidelines.[225]

Typically, no written contracts, nondisclosure agreements, or employee agreements are made with crowdworkers. For users of the Amazon Mechanical Turk, this means that employers decide whether users' work is acceptable and reserve the right to withhold pay if it does not meet their standards.[237] Critics say that crowdsourcing arrangements exploit individuals in the crowd, and a call has been made for crowds to organize for their labor rights.[238][197][239]

Collaboration between crowd members can also be difficult or even discouraged, especially in the context of competitive crowd sourcing. Crowdsourcing site InnoCentive allows organizations to solicit solutions to scientific and technological problems; only 10.6% of respondents reported working in a team on their submission.[194] Amazon Mechanical Turk workers collaborated with academics to create a platform, WeAreDynamo.org, that allows them to organize and create campaigns to better their work situation, but the site is no longer running.[240] Another platform run by Amazon Mechanical Turk workers and academics, Turkopticon, continues to operate and provides worker reviews on Amazon Mechanical Turk employers.[241]

America Online settled the case Hallissey et al. v. America Online, Inc. for $15 million in 2009, after unpaid moderators sued to be paid the minimum wage as employees under the U.S. Fair Labor Standards Act.

Crowdsourcing has also been increasingly used in artificial intelligence training, where large datasets of human-labeled images or text are collected to improve machine learning models[242].

Other concerns

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Besides insufficient compensation and other labor-related disputes, there have also been concerns regarding privacy violations, the hiring of vulnerable groups, breaches of anonymity, psychological damage, the encouragement of addictive behaviors, and more.[243] Many but not all of the issues related to crowdworkes overlap with concerns related to content moderators.

See also

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References

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[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Crowdsourcing is the practice of delegating tasks, problems, or decision-making traditionally handled by designated agents—such as employees or specialists—to a large, undefined group of participants, typically through open online calls that leverage collective input for solutions, ideas, or labor. The term was coined in 2006 by journalist Jeff Howe in a Wired magazine article, combining "crowd" and "outsourcing" to describe harnessing distributed human intelligence instead of centralized expertise. This approach has enabled notable achievements across domains, including business innovation through platforms like , where user-submitted designs have led to commercial products, and scientific challenges such as NASA's crowdsourced solutions for astronaut communication or asteroid mapping, demonstrating the crowd's capacity to generate viable, cost-effective outcomes beyond individual experts. Empirical studies affirm its effectiveness for specific tasks like idea generation and data annotation, where aggregation of diverse perspectives can outperform small expert groups under proper incentives, as seen in peer-reviewed analyses of testing and . However, crowdsourcing's defining characteristics include reliance on digital platforms for , implicit incentives like rewards or recognition to motivate participation, and inherent variability in output quality due to participants' heterogeneous skills and motivations. Despite successes, crowdsourcing faces controversies rooted in causal factors like misaligned incentives and task complexity, often yielding low-quality or exploitative results; for instance, microtask platforms such as have drawn criticism for underpaying workers—sometimes below —while producing unreliable data for complex analyses, as evidenced by systematic reviews highlighting "dark side" outcomes including poor coordination and ethical lapses in global labor distribution. Studies underscore that while crowds excel in simple, parallelizable tasks, they frequently underperform for nuanced or creative endeavors without robust filtering, privileging volume over precision and risking systemic biases from participant demographics or platform algorithms.

Definition and Core Concepts

Formal Definition and Distinctions

Crowdsourcing is defined as the act of transferring a function traditionally performed by an employee or contractor to an undefined, generally large group of people via an open call, often leveraging internet platforms to aggregate contributions of ideas, labor, or resources. This concept was coined by journalist Jeff Howe in a 2006 Wired magazine article, combining "crowd" and "outsourcing" to describe a distributed problem-solving model that emerged with digital connectivity. Core to the definition are four elements: an identifiable organization or sponsor issuing the call; a task amenable to distributed execution; an undefined pool of potential solvers drawn from the public; and a mechanism for aggregating and evaluating contributions, which may involve incentives like monetary rewards or recognition. Unlike traditional , which contracts specific, predefined entities or firms for specialized work with negotiated terms, crowdsourcing solicits input from an anonymous, self-selecting multitude without prior selection, emphasizing and diversity over reliability of a fixed provider. This distinction arises from causal differences in coordination: outsourcing relies on hierarchical contracts and to a bounded group, whereas crowdsourcing exploits the statistical for emergent solutions, though it risks lower individual and variable quality. Crowdsourcing further differs from open-source development, which typically involves voluntary, peer-driven collaboration on shared codebases by a self-organizing of experts, often without a central sponsor directing specific tasks. In crowdsourcing, the sponsor retains control over task definition and selection, potentially compensating participants selectively, whereas prioritizes communal ownership and iterative forking without monetary exchange as the primary motivator. It also contrasts with platforms, where contributions are unsolicited and platform-agnostic, as crowdsourcing structures participation around explicit, bounded problems to harness targeted collective output. These boundaries highlight crowdsourcing's reliance on mediated openness for efficiency gains, grounded in empirical observations of platforms like , launched in 2005, which formalized micro-task distribution to global workers.

Underlying Principles

Crowdsourcing operates on the principle that distributed groups of individuals, when properly structured, can generate superior solutions, predictions, or judgments compared to isolated experts or centralized authorities, a phenomenon rooted in the aggregation of diverse, independent inputs. This draws from the "wisdom of crowds" concept, empirically demonstrated in Francis Galton's 1906 observation at a county fair where 787 attendees guessed the dressed weight of an ox; the average estimate of 1,197 pounds deviated by just 0.8% from the actual 1,199 pounds, illustrating how uncorrelated errors tend to cancel out in large samples. The mechanism relies on statistical properties: individual biases or inaccuracies, if not systematically correlated, diminish through averaging, yielding a collective estimate with reduced variance akin to the applied to judgments. James Surowiecki formalized the conditions enabling this in his 2004 analysis, identifying four essential elements: diversity of opinion, which introduces varied perspectives to mitigate uniform blind spots; independence, preventing conformity or herding that amplifies errors; , allowing local knowledge to inform contributions without top-down distortion; and aggregation, via simple mechanisms like voting or averaging to synthesize inputs into coherent outputs. In crowdsourcing applications, platforms enforce these by issuing open calls to heterogeneous participants—often strangers with no prior coordination—to submit independent responses, then computationally aggregate them, as seen in prediction markets or idea contests where crowd forecasts have outperformed individual analysts by margins of 10-30% in domains like election outcomes or economic indicators. Causal realism underscores that success hinges on these conditions; violations, such as informational cascades where early opinions sway later ones, revert crowds to the quality of their most influential subset, as evidenced by experiments where without increases error rates by up to 20%. Thus, effective crowdsourcing designs incorporate incentives for truthful revelation—monetary rewards calibrated to task complexity or reputational feedback—to sustain and participation, while filtering for diversity through broad recruitment rather than homogeneous networks. Empirical studies confirm that crowds under these principles solve complex problems, such as image labeling or optimization tasks, with accuracy rivaling specialized algorithms when scaled to thousands of contributors.

Historical Development

Pre-Modern Precursors

In , the agora functioned as a central public forum from the onward, where citizens gathered for announcements, debates, and the exchange of ideas on governance, trade, and community issues, enabling distributed input from a broad populace prior to formalized hierarchies dominating decision-making. During China's (618–907 AD), joint-stock companies emerged as an early financing model, allowing multiple individuals to contribute capital to large-scale enterprises such as maritime expeditions or infrastructure projects, distributing risk and rewards across participants in a manner resembling proto-crowdfunding. In 1567, King launched an open competition with a cash prize for the best design of a fortified city to counter Dutch revolts, soliciting architectural and defensive proposals from engineers and experts across his empire, which demonstrated the efficacy of monetary incentives in aggregating specialized knowledge from a dispersed group. These instances relied on public dissemination of problems and rewards to motivate voluntary contributions, though limited by communication constraints and elite oversight, they prefigured crowdsourcing by leveraging collective capacities beyond centralized authority for practical solutions.

19th-20th Century Examples

In the mid-19th century, the compilation of the represented a pioneering effort to crowdsource linguistic documentation. Initiated by the Philological Society in 1857, the project solicited volunteers worldwide to extract and submit quotation slips from books and other printed sources, illustrating historical word usage, meanings, and etymologies. James Murray, appointed chief editor in 1879, systematized the influx of contributions, which ultimately exceeded five million slips from thousands of participants, including amateurs, scholars, and readers across social classes. This distributed labor enabled the dictionary's incremental publication starting with fascicles in 1884, culminating in the complete 10-volume first edition in 1928, though delays arose from the volume of unverified submissions and editorial rigor. Meteorological data collection in the 19th century also harnessed dispersed volunteer networks, prefiguring modern citizen science as a form of crowdsourcing for empirical observation. In the United States, the Smithsonian Institution under Secretary Joseph Henry coordinated a voluntary observer corps from the 1840s, with participants recording daily weather metrics like temperature, pressure, and precipitation at remote stations. This expanded under the U.S. Army Signal Corps in 1870, which oversaw approximately 500 stations—many operated by unpaid civilians—yielding datasets for national weather maps and storm predictions until the Weather Bureau's formation in 1891. Similar initiatives in Britain, supported by the Royal Society and local scientific societies, relied on amateur meteorologists to furnish observations, compensating for the limitations of centralized instrumentation and enabling broader spatial coverage for climate analysis. Into the 20th century, prize competitions emerged as structured crowdsourcing for technological breakthroughs, exemplified by aviation incentives. The Orteig Prize, announced in 1919 by hotelier Raymond Orteig, offered $25,000 (equivalent to about $450,000 in 2023 dollars) for the first nonstop flight between New York City and Paris, attracting entrants who iterated on aircraft designs and navigation methods. Charles Lindbergh claimed the award on May 21, 1927, after eight years of competition spurred advancements in monoplane construction and long-range fuel systems. Concurrently, social research projects like Mass-Observation, founded in Britain in 1937 by anthropologists Tom Harrisson and Charles Madge alongside poet Humphrey Jennings, crowdsourced behavioral data through a panel of around 500 volunteer observers who maintained diaries and conducted unobtrusive public surveillance. This yielded thousands of reports on everyday attitudes and habits until the organization's core activities waned in the early 1950s, providing raw material for sociological insights amid World War II rationing and morale studies.

Emergence in the Digital Age (2000s Onward)

The advent of widespread and technologies in the early 2000s facilitated the shift of crowdsourcing from niche applications to scalable digital platforms, enabling organizations to tap distributed networks for tasks ranging from content creation to problem-solving. Early examples included , launched in 2000, which crowdsourced t-shirt designs by soliciting submissions from artists and using community votes to select designs for production and sale. Similarly, iStockphoto, also founded in 2000, allowed amateur photographers to upload and sell stock images, disrupting traditional agencies by aggregating user-generated visual content. The term "crowdsourcing" was formally coined in June 2006 by journalist Jeff Howe in a Wired magazine article, defining it as the act of outsourcing tasks once performed by specialized employees to a large, undefined crowd over the internet, often for lower costs and innovative outcomes. This conceptualization built on prior platforms like InnoCentive, established in 2001 as a spin-off from Procter & Gamble, which posted scientific and technical challenges to a global network of solvers, awarding prizes for solutions to R&D problems that internal teams could not resolve. Wikipedia, launched in January 2001, exemplified collaborative knowledge production by permitting anonymous volunteers to edit articles, resulting in a repository exceeding 6 million English-language entries by amassing incremental contributions from millions of users. Amazon Mechanical Turk (MTurk), publicly beta-launched on November 2, 2005, marked a pivotal development in microtask crowdsourcing, providing a for " tasks" (HITs) such as image labeling, transcription, and surveys, completed by remote workers for micropayments, which enabled of processes requiring human judgment at reduced scale compared to full-time hires. By the late 2000s, these mechanisms expanded into , with Kickstarter's founding in 2009 introducing reward-based funding models where creators pitched projects to backers, who pledged small amounts in exchange for prototypes or perks, channeling over $8 billion in commitments to hundreds of thousands of initiatives by the 2020s. Such platforms demonstrated crowdsourcing's efficiency in leveraging voluntary or incentivized participation, though they also highlighted challenges like and worker exploitation in low-pay tasks.

Theoretical Foundations

Economic Incentives and Participant Motivations

Economic incentives in crowdsourcing encompass monetary payments designed to elicit contributions from distributed participants, addressing challenges such as low coordination and free-riding inherent in decentralized systems. Microtask platforms like employ piece-rate compensation, where workers receive payments ranging from $0.01 to $0.10 per human intelligence task (HIT), yielding median hourly earnings of $3.01 for U.S.-based workers and $1.41 for those in , based on analyses of platform data. These rates reflect requester-set pricing, which prioritizes cost efficiency but often results in effective wages below minimum standards in high-income countries. In prize contests, such as those hosted on InnoCentive, incentives take the form of fixed bounties awarded to top solutions, with typical prizes averaging $20,000 and select challenges offering up to $100,000 or more for breakthroughs in areas like or resilience technologies. Such economic mechanisms primarily influence participation volume rather than output quality, as empirical experiments demonstrate that higher bonuses increase task completion rates but yield negligible improvements in accuracy or effort. For instance, field studies on crowdsourcing platforms show that financial rewards mitigate dropout in low-skill tasks but fail to sustain high-effort contributions without complementary designs like performance thresholds or lotteries. Non-monetary economic variants, including reputational credits convertible to future opportunities or self-selected rewards like vouchers, have been tested to enhance engagement; one multi-study analysis found ideators prefer flexible non-cash options when available, potentially boosting solution diversity over pure cash payouts. Participant motivations in crowdsourcing extend beyond to include intrinsic drivers like task enjoyment, skill acquisition, and social recognition, alongside extrinsic factors such as and belonging. A of quantitative studies across platforms reveals that intrinsic motivations, particularly enjoyment, exhibit stronger correlations with sustained participation (effect sizes around 0.30-0.40) than purely financial incentives in voluntary or contest-based settings. and experience moderate these effects; for example, novices may prioritize monetary gains, while experts in ideation contests respond more to recognition and challenge complexity. Empirical surveys of users on online platforms classify motivations into reward-oriented (e.g., or status) and requirement-oriented (e.g., problem-solving ) categories, with the former dominating microtasks and the latter prevailing in where participants self-select high-value problems. Hybrid motivations often yield optimal outcomes, as pure economic incentives risk attracting low-quality contributors or encouraging strategic withholding, while intrinsic appeals foster long-term ecosystems. Studies on contest platforms indicate that combining prizes with public acknowledgment increases solver diversity and solution appropriateness, though over-reliance on money can crowd out voluntary contributions in domains like . Systematic reviews of motivational theories applied to crowdsourcing highlight the long-tail distribution of engagement, where a minority of highly motivated participants (driven by passion or ) generate disproportionate value, underscoring the limits of uniform economic incentives.

Mechanisms of Collective Intelligence

Collective intelligence in crowdsourcing emerges when mechanisms systematically harness diverse individual inputs to produce judgments or solutions that surpass those of solitary experts or centralized . These mechanisms rely on foundational conditions outlined by , including diversity of opinion—where participants bring varied perspectives to counteract uniform biases—independence of judgments to prevent informational cascades, decentralization to incorporate localized knowledge, and effective aggregation to synthesize inputs into coherent outputs. Failure in any condition, such as excessive interdependence, can lead to and diminished accuracy, as observed in scenarios where overrides private information. Empirical evidence underscores these principles' efficacy under proper implementation. In Francis Galton's 1907 analysis of a livestock fair contest, 787 participants guessed the dressed weight of an ox; the crowd's mean estimate of 1,197 pounds deviated by just 1 pound from the true 1,198 pounds, illustrating how averaging independent estimates aggregates probabilistic accuracy despite individual errors. Similarly, in controlled simulations of crowdsourcing as collective problem-solving, intelligence manifests through balanced collaboration: small groups (around 5 members) excel in easy tasks via high collectivism, while larger assemblies (near 50 participants) optimize for complex problems by mitigating free-riding through fitness-based selection, yielding higher overall capacity than purely individualistic or overly collective approaches. Aggregation techniques form the operational core, transforming raw contributions into reliable . For quantitative estimates, simple averaging or calculations suffice when holds, as in tasks; for categorical judgments, voting or probabilistic models like Dawid-Skene— which infer true labels from worker reliability estimates—enhance precision in noisy data environments. In decentralized platforms, mechanisms such as iterative synthesis allow parallel idea generation followed by sequential refinement, fostering emergent quality; evaluative voting then filters outputs, as seen in architectural crowdsourcing where network-based systems reduced deviation from optimal artifacts (e.g., collective distance metric dropping from 0.514 to 0.283 over 10 iterations with 6 contributors). Prediction markets extend this by aggregating via incentive-aligned trading, where share prices reflect crowd consensus probabilities, often outperforming polls in forecasting events like elections. These mechanisms' success hinges on causal factors like participant incentives and task structure, with empirical studies showing that hybrid approaches—combining discussive elements (e.g., Q&A for clarification) with synthetic —outperform solo efforts in creative domains, provided diversity is maintained to avoid convergence on suboptimal local optima. In practice, platforms mitigate biases through or randomized ordering to preserve independence, though real-world deviations, such as homogeneous participant pools, can undermine outcomes, emphasizing the need for deliberate design over naive scaling.

Comparative Advantages Over Traditional Hierarchies

Crowdsourcing leverages the collective intelligence of diverse participants, often yielding superior outcomes compared to the centralized decision-making in traditional hierarchies, where information bottlenecks and cognitive biases limit effectiveness. James Surowiecki's framework in The Wisdom of Crowds posits that under conditions of diversity of opinion, independence, decentralization, and effective aggregation, group judgments outperform individual experts or hierarchical elites, as demonstrated in empirical examples like market predictions and estimation tasks where crowds achieved errors as low as 1-2% versus experts' higher variances. This advantage stems from crowdsourcing's ability to draw from a broader knowledge base, mitigating the "status-knowledge disconnect" prevalent in hierarchies where deference to authority suppresses novel insights. In terms of speed, crowdsourcing enables parallel processing of problems by distributing tasks across a global pool, contrasting with the serial workflows of hierarchical organizations that constrain to internal layers of approval. Studies indicate that crowdsourcing platforms facilitate rapid idea generation and , with organizations reporting faster problem resolution—often in weeks rather than months—due to real-time contributions from thousands of participants. For instance, in innovation contests, crowd-sourced solutions emerge 2-5 times quicker than internal R&D cycles in firms reliant on top-down directives. Cost advantages arise from outcome-based incentives, such as prizes or micro-payments, which avoid the overhead of maintaining salaried hierarchies; empirical analyses show crowdsourcing reduces expenses by 50-90% for tasks like labeling or challenges while scaling to volumes unattainable internally. This model accesses specialized skills on-demand without long-term commitments, particularly beneficial for knowledge-based industries where traditional hiring lags behind dynamic needs. Furthermore, crowdsourcing fosters organizational learning across individual, group, and firm levels by integrating external feedback loops, enhancing adaptability in ways hierarchies struggle with due to insular flows. Quantitative from local governments and firms reveals positive correlations between crowd participation mechanisms—like voting and creation—and improved learning outcomes, with effect sizes indicating 20-30% gains in over siloed approaches. These benefits, however, depend on robust aggregation to filter noise, underscoring crowdsourcing's edge in harnessing absent in rigid command structures.

Types and Mechanisms

Explicit Crowdsourcing Methods

Explicit crowdsourcing methods involve the intentional of contributions from a distributed group of participants who are aware of their in addressing defined tasks or challenges, typically through structured platforms that facilitate task assignment, , and aggregation. These approaches contrast with implicit methods by requiring active, deliberate , often motivated by financial incentives, prizes, recognition, or voluntary interest. Common implementations include microtask marketplaces, prize contests, and volunteer-based collaborations, enabling organizations to leverage collective effort for scalable outcomes in , , and . Microtasking platforms represent a core explicit method, breaking complex work into discrete, low-skill units such as image annotation, transcription, or , distributed to workers via online marketplaces. Amazon Mechanical Turk, launched on November 2, 2005, pioneered this model by providing requesters access to a global pool of participants for tasks (HITs), with payments typically ranging from cents to dollars per task. By enabling rapid completion of repetitive yet judgment-requiring activities, MTurk has supported applications in data labeling and , though worker compensation averages below in many cases due to competitive bidding. Prize contests form another explicit mechanism, where problem owners post challenges with monetary rewards for optimal solutions, attracting specialized solvers from diverse fields. InnoCentive, developed from Eli Lilly's internal R&D experiments in the early 2000s and publicly operational since 2007, exemplifies this by hosting open calls for technical innovations, with awards often exceeding $100,000. The platform has facilitated over 2,500 solved challenges across industries like pharmaceuticals and , achieving an 80% success rate by drawing on a network of more than 400,000 solvers as of 2025. Such contests promote efficient , as payment occurs only upon success, though they may favor incremental over radical breakthroughs due to predefined criteria. Volunteer collaborations constitute a non-monetary explicit variant, relying on intrinsic motivations like scientific curiosity or community building to elicit contributions for knowledge-intensive tasks. Galaxy Zoo, a project launched in July 2007, engages participants in classifying galaxy morphologies from images, amassing classifications for over 125 million galaxies by 2017 and enabling discoveries such as unusual galaxy types leading to more than 60 peer-reviewed papers. This method harnesses domain-specific expertise from non-professionals, yielding high-volume outputs at low cost, but requires robust quality controls like consensus voting to mitigate errors from untrained contributors.

Implicit and Hybrid Approaches

Implicit crowdsourcing harnesses contributions from participants unaware of their role in or problem-solving, relying on passive behaviors such as app interactions, sensor readings, or engagements rather than deliberate tasks. This method extracts value from incidental user actions, like location traces from smartphones or implicit feedback in games, to build datasets or models without explicit or incentives. Unlike explicit crowdsourcing, it minimizes participant burden but requires robust backend algorithms to infer and validate signals from noisy, unstructured inputs. Key mechanisms include behavioral observation and automated labeling; for instance, in indoor localization, implicit crowdsourcing collects radio fingerprints from pedestrians' devices during normal movement, labeling them via contextual data like floor changes detected by sensors, achieving maps with 80-90% accuracy in tested environments as of 2021. Another application identifies abusive content in social networks by monitoring natural user blocks or reports as implicit signals, with a 2020 framework reporting detection rates up to 85% by aggregating these without user prompts. Similarly, rumor detection leverages sharing patterns and credibility cues from user interactions, as demonstrated in a 2020 IEEE study on data where implicit metrics outperformed some explicit labeling baselines. Hybrid crowdsourcing blends implicit and explicit techniques, or integrates crowds with algorithmic processes, to balance scale, accuracy, and cost. This approach often uses implicit for broad coverage and explicit input for verification, or employs crowds to refine machine outputs iteratively. For example, in network visualization for biological data, the 2021 Flud system combines crowd-sourced layout adjustments with energy-minimizing algorithms, reducing optimization time by 40-60% over pure computational methods in experiments on protein interaction graphs. In , hybrid methods merge crowdsourced seismic recordings from smartphones with professional sensors, as reviewed in a 2018 showing improved detection resolution by integrating voluntary explicit submissions with implicit device vibrations, covering gaps in traditional networks. For weather estimation, the framework of 2013 uses participatory sensing where explicit user reports hybridize with implicit mobile sensor streams, yielding estimates within 10-20% error margins in urban tests. These hybrids mitigate limitations like implicit data sparsity through targeted explicit interventions, enhancing overall reliability in dynamic environments.

Specialized Variants (e.g., Crowdfunding, Prize Contests)

Crowdfunding constitutes a financial variant of crowdsourcing, whereby project initiators appeal to a dispersed online audience for small monetary pledges to realize ventures ranging from creative endeavors to startups, often in exchange for rewards or equity. This mechanism diverges from general crowdsourcing by prioritizing capital aggregation over contributions of ideas, skills, or content, with campaigns typically featuring fixed deadlines and all-or-nothing funding models to mitigate partial fulfillment risks. The approach gained traction post-2008 as an alternative to traditional , with platforms like —launched in April 2009—enabling over 650,000 projects and accumulating approximately $7 billion in pledges by 2023. Globally, the crowdfunding sector expanded to $20.3 billion in transaction volume by 2023, driven by reward-based, equity, and debt models, though success rates hover around 40-50% due to factors like market saturation and unproven viability. Prize contests represent another specialized crowdsourcing modality, deploying fixed monetary incentives to solicit solutions from broad participant pools for complex challenges, thereby harnessing competitive dynamics to accelerate breakthroughs unattainable via conventional R&D. Participants invest resources upfront without guaranteed remuneration, with awards disbursed solely to those meeting rigorous, verifiable milestones, which incentivizes high-risk innovation while minimizing sponsor costs until success. The , founded in 1996 by , pioneered modern iterations, issuing over $250 million in prize purses across 30 competitions by 2024, including the $10 million Ansari XPRIZE claimed in 2004 by for suborbital flight and the $100 million Carbon Removal XPRIZE awarded on April 23, 2025, to teams demonstrating gigaton-scale CO2 extraction. Complementary examples include NASA's Centennial Challenges, initiated in 2005, which have distributed over $50 million for advancements in robotics and propulsion, and historical precedents like the 1714 Longitude Prize yielding John Harrison's for navigational accuracy. These variants extend crowdsourcing's core by aligning participant efforts with tangible outputs—funds in or prototypes in s—yet both face limits from participant fatigue and selection biases favoring viral appeal over substantive merit. Empirical analyses indicate contests yield 10-30 times the in spurred advancements compared to grants, though outcomes depend on clear criteria and diverse entrant pools. , meanwhile, democratizes access but amplifies risks of or unfulfilled promises, with regulatory frameworks like the U.S. JOBS Act of 2012 enabling equity models while imposing disclosure mandates.

Applications and Case Studies

Business and Product Innovation

Crowdsourcing has been applied in and product to source ideas, designs, and solutions from distributed networks of participants, often reducing internal R&D costs and accelerating development cycles. Companies post challenges or solicit submissions on platforms, evaluating contributions based on feedback, expert review, or market potential. Empirical studies indicate that such approaches can yield higher success rates by tapping diverse external expertise, though outcomes depend on effective structures and selection mechanisms. Procter & Gamble's Connect + Develop program, initiated in 2000, exemplifies through by partnering with external entities including individuals, startups, and research institutions to co-develop products. The initiative has resulted in over 1,000 active agreements, more than doubling P&G's success rate while reducing R&D spending as a percentage of sales from 4.8% to lower levels through decreased internal invention reliance. This shift sourced approximately 35% of innovations externally by the mid-2000s, enabling breakthroughs in consumer goods like and variants via crowdsourced problem-solving. LEGO Ideas, launched in 2008, allows fans to submit and vote on product concepts, with designs reaching 10,000 supporters advancing to review by LEGO's development team for potential commercialization. This platform has produced sets like the NASA Apollo Saturn V and Central Perk from Friends, contributing to LEGO's revenue growth to $9.5 billion in 2022, a 17% increase partly attributed to crowdsourced hits that reduced development timelines by up to fourfold compared to traditional processes. By 2023, over 49 ideas had qualified for review in a four-month span, demonstrating scalable idea validation through user engagement. Platforms like InnoCentive facilitate by hosting prize-based challenges for technical solutions, achieving an 80% success rate across over 2,500 solved problems since 2000 and generating 200,000 innovations. In contexts, this has supported advancements in materials and processes, with 70% of solutions often originating from solvers outside the seeker's field, enhancing novelty and cost-efficiency. , operational since 2000, crowdsources apparel designs via community scoring, printing top-voted submissions and awarding creators $2,000 or more, which has sustained a model by minimizing risks through demand-driven production.

Scientific and Technical Research

Crowdsourcing in scientific research primarily leverages distributed for tasks such as , data annotation, and iterative problem-solving, where automated algorithms struggle with ambiguity or novelty. Platforms enable non-experts to contribute via gamified interfaces or simple tools, processing vast datasets that would otherwise overwhelm individual researchers or labs. This approach has yielded empirical successes in fields like astronomy and biochemistry, with verifiable outputs including peer-reviewed structures and classifications validated against professional benchmarks. In , the platform, developed in 2008 by researchers at the , crowdsources puzzles through a competitive gaming interface. Players manipulate three-dimensional protein models to minimize energy states, drawing on intuitive spatial reasoning. A landmark achievement occurred in 2011 when Foldit participants generated accurate models of a monomeric retroviral protease from the Mason-Pfizer monkey virus, enabling molecular replacement and determination—a problem unsolved by computational methods despite over 10 years of effort. The resulting structure, resolved at 1.6 resolution, revealed a novel fold distinct from dimeric homologs, aiding insights into retroviral maturation. This success stemmed from players devising new algorithmic strategies during gameplay, which were later formalized into software improvements. Extending this, a 2019 study involved 146 Foldit designs encoded as synthetic genes; 56 expressed soluble, monomeric proteins in E. coli, adopting 20 distinct folds—including one unprecedented in nature—with high-resolution validations matching player predictions (Cα-RMSD 0.9–1.7 ). These outcomes underscore crowdsourcing's capacity for de novo design, where human creativity addresses local strain issues overlooked by physics-based simulations. Astronomy has seen extensive application through citizen science, notably Galaxy Zoo, launched in 2007 to classify galaxies from the . Over 150,000 volunteers delivered more than 50 million classifications in the first year alone, with subsequent iterations like Galaxy Zoo 2 adding 60 million in 14 months; these match expert reliability and have fueled over 650 peer-reviewed publications. Key discoveries include "green pea" galaxies—compact, high-redshift objects indicating rapid —and barred structures in distant galaxies, challenging models of cosmic evolution and securing follow-up observations from telescopes like Hubble and . The broader platform, encompassing Galaxy Zoo, facilitated the 2018 detection of a five-planet exoplanet system via the Exoplanet Explorers project, where volunteers analyzed Kepler light curves to identify transit signals missed by initial algorithms. Such efforts demonstrate scalability, with crowds processing petabytes of imaging data to reveal serendipitous patterns, though outputs require statistical debiasing to mitigate volunteer inconsistencies. In technical research domains like and , crowdsourcing supports hybrid human-machine workflows, as in Zooniverse's Milky Way Project, where annotations of bubbles advanced star-formation models. Empirical metrics show crowds achieving 80-90% agreement with experts on visual tasks, accelerating testing by orders of magnitude compared to solo efforts. However, success hinges on task decomposition and incentive alignment, with boosting retention but not guaranteeing domain-generalizable insights. These applications highlight causal advantages in harnessing collective for ill-posed problems, though integration with computational verification remains essential for rigor.

Public Policy and Governance

Governments have increasingly adopted crowdsourcing to solicit public input on policy design, resource allocation, and problem-solving, aiming to leverage collective wisdom for more responsive governance. In the United States, Challenge.gov, launched in 2010 pursuant to the America COMPETES Reauthorization Act, serves as a federal platform where agencies post challenges with monetary prizes to crowdsource solutions for public sector issues, such as disaster response innovations and regulatory improvements; by 2023, it had facilitated over 1,500 challenges with total prizes exceeding $500 million. Similarly, Taiwan's vTaiwan platform, initiated in 2014, employs tools like Pol.is for online deliberation on policy matters, notably contributing to the 2016 Uber regulations through consensus-building among 20,000 participants, which informed legislative drafts and enhanced perceived democratic legitimacy. Notable experiments include Iceland's 2011-2013 constitutional revision, where a 950-member National Forum crowdsourced core principles, followed by a 25-member incorporating online public submissions from over 39,000 visitors to draft a new document; the proposal garnered 67% approval in a 2012 advisory but failed parliamentary in 2013 amid political opposition and procedural disputes, highlighting implementation barriers despite high engagement. , blending crowdsourcing with , originated in , , in 1989 and has expanded digitally in cities like and , where residents propose and vote on budget allocations via apps; evaluations show boosts in participation rates—e.g., Warsaw's 2016-2020 cycles drew over 100,000 votes annually—but uneven outcomes, with funds often favoring visible infrastructure over systemic equity due to self-selection biases among participants. During the , public administrations in and used crowdsourcing for targeted responses, such as Italy's 2020 call for mask distribution ideas and the UK's NHS volunteer mobilization platform, which recruited 750,000 participants in days; these efforts yielded practical innovations but revealed limitations in scaling unverified inputs amid crises. Empirical analyses indicate crowdsourcing enhances organizational learning and policy novelty in government settings, with studies across disciplines finding positive correlations to citizen empowerment and legitimacy when platforms ensure moderation, though effectiveness diminishes without mechanisms for representativeness and elite buy-in. Failures, like Iceland's, underscore causal risks: crowdsourced outputs often lack binding , vulnerable to by entrenched interests, and may amplify vocal minorities over broader consensus.

Other Domains (e.g., Journalism, Healthcare)

In journalism, crowdsourcing facilitates public involvement in data gathering, verification, and investigative processes, often supplementing traditional reporting with distributed expertise. During crises, such as the , journalists integrated crowdsourced reports to map events and disseminate verified information, with analyses showing that professional intermediaries enhanced the reliability of volunteer-submitted data by filtering and contextualizing inputs. Early experiments like Off the Bus in 2008 demonstrated viability, where citizen contributors broke national stories for mainstream outlets, though success depended on editorial oversight to mitigate inaccuracies inherent in unvetted submissions. More recent applications include crowdsourced , which empirical studies indicate can scale verification efforts effectively when structured with clear protocols, outperforming individual assessments in detecting across diverse content. In healthcare, crowdsourcing supports by harnessing non-expert input for tasks like , challenges, and real-world , shifting from insular expert models to . Systematic reviews identify key applications in —via crowds images for algorithmic through self-reported symptoms, and , where platforms solicit molecular designs from global participants, yielding solutions comparable to specialized labs in cases like puzzles solved via gamified interfaces. For instance, crowdsourcing has accelerated target identification in , with one 2016 initiative at involving public of genomic datasets to uncover novel drug candidates, demonstrating feasibility despite challenges in control. Quantitative evidence from reviews confirms modest but positive health impacts, such as improved outbreak detection via apps aggregating patient data, though outcomes vary with participant incentives and validation mechanisms to counter biases like self-selection in reporting.

Empirical Benefits and Impacts

Economic Efficiency and Innovation Gains

Crowdsourcing improves by distributing tasks to a large, distributed , often at lower marginal costs than maintaining specialized internal teams. Platforms facilitate access to global talent without fixed overheads, enabling reductions through efficient matching and on-demand participation. Empirical analyses of crowdsourcing marketplaces highlight strengths in labor accessibility and cost-effectiveness, as tasks are completed via competitive bidding or fixed prizes rather than salaried positions. In prize-based systems like InnoCentive, seekers post R&D challenges with bounties that typically yield solutions at fractions of internal development expenses. A 2009 Forrester Consulting study of InnoCentive's model found an average 74% , driven by accelerated problem-solving and avoidance of sunk costs in unsuccessful internal trials. Similarly, applications have reported up to 182% ROI with payback periods under two months, alongside multimillion-dollar gains over multi-year horizons. Crowdsourcing drives innovation gains by harnessing heterogeneous knowledge inputs, surpassing the limitations of siloed expertise. Diverse participant pools generate novel solutions through parallel ideation, with reviews confirming enhanced accuracy, scalability, and boundary-transcending outcomes in research tasks. Organizational studies demonstrate positive causal links to learning at individual, group, and firm levels, fostering feed-forward innovation processes. In product domains, such as Threadless's design contests, community-sourced ideas reduce time-to-market by validating demand via votes before production, yielding higher hit rates than traditional forecasting.

Scalability and Diversity Advantages

Crowdsourcing enables the distribution of complex tasks across vast participant pools, facilitating scalability beyond the constraints of traditional teams or organizations. Platforms such as allow for rapid engagement of global workers at low costs, with micro-tasks often compensated at rates as low as $0.01, enabling real-time processing of large datasets that would otherwise require prohibitive resources. For example, the Galaxy Zoo project mobilized volunteers to classify nearly 900,000 galaxies, achieving research-scale outputs unattainable by small expert groups and demonstrating how crowds can handle voluminous data in fields like astronomy. This scalability supports expansion or contraction of efforts based on demand, as seen in data annotation for , where crowds meet surging needs for labeled datasets that outpace internal capacities. The global reach of crowdsourcing inherently incorporates participant diversity in demographics, expertise, and viewpoints, yielding advantages in and comprehensive problem-solving. Diverse teams outperform homogeneous ones in covering multifaceted skills and perspectives, with algorithmic approaches ensuring maximal diversity while fulfilling task requirements, as validated through scalable experimentation. Exposure to diverse knowledge in crowdsourced challenges directly enhances solution innovativeness, evidenced by a regression of β = 1.19 (p < 0.01) across 3,200 posts from 486 participants in 21 contests, where communicative participation further amplifies serial knowledge integration leading to breakthrough ideas. Similarly, cognitive diversity among crowd reviewers boosts identification of societal impacts from algorithms, with groups of five diverse evaluators averaging 8.7 impact topics versus about 3 from one, underscoring beyond optimal diversity thresholds. These scalability and diversity dynamics combine to drive empirical gains in accuracy and discovery, as diverse crowds have achieved up to 97.7% correctness in collective judgments with large contributor volumes, transcending geographic and institutional boundaries for applications like medical diagnostics. In governmental settings, such approaches foster multi-level learning—individual, group, and organizational—through varied inputs, with confirming positive effects across crowdsourcing modes like wisdom crowds and voting.

Verified Success Metrics and Examples

InnoCentive, a crowdsourcing platform for R&D challenges, has resolved over 2,500 problems with an 80% success rate, delivering more than 200,000 innovations and distributing $60 million in awards to solvers as of June 2025. A Forrester Consulting study commissioned by InnoCentive in 2009 found that its challenge-driven approach yielded a 74% for participating organizations by accelerating research at lower costs compared to internal efforts. For instance, the posted 10 challenges between 2006 and 2009, achieving solutions in 80% of cases through diverse solver contributions. In scientific applications, the online game has enabled non-expert participants to outperform computational algorithms in and design. Top players solved challenging refinement problems requiring backbone rearrangements, achieving lower energy states than automated methods in benchmarks published in 2010. By 2011, players independently discovered symmetrization strategies and novel algorithms for tasks like modeling the AIMD monkey virus protease, with successful player-derived recipes rapidly propagating across the community and dominating solutions. A notable 2012 achievement involved crowdsourced redesign of a microbial to degrade retroviral , providing a potential treatment avenue in just weeks, far faster than expert-only approaches. Business-oriented crowdsourcing, such as 's t-shirt design contests, demonstrates commercial viability through community voting that correlates with revenue generation. Analysis of data shows that crowd scores predict design sales, with high-voted submissions yielding skewed positive revenue distributions upon production. At its peak, the platform selected about 150 designs annually for printing, sustaining operations by aligning with market demand without traditional design teams. Over 13 years to 2013, distributed $7.12 million in prizes to contributors, reflecting scalable output from voluntary participation.
PlatformKey MetricAchievement
InnoCentive80% challenge success rate2,500+ solutions, $60M awards (2025)
Superior algorithm discoveryNovel protein redesigns in weeks vs. years
Vote-revenue correlation150 annual designs, $7.12M payouts (to 2013)

Challenges and Criticisms

Quality Control and Output Reliability

Crowdsourced outputs frequently suffer from inconsistencies arising from heterogeneous worker abilities, varying effort levels, and misaligned incentives, such as rapid completion for monetary rewards leading to spam or superficial responses. In microtask platforms like , worker error rates can exceed 20-30% in unsupervised settings for classification tasks without intervention, as heterogeneous skills amplify variance in responses. Open-ended tasks exacerbate this, where subjective interpretations yield multiple valid answers but low inter-worker agreement, often below 70% due to contextual dependencies and lack of standardized evaluation. Quality assurance mechanisms address these through worker screening via qualification tests or "" tasks with known answers to filter unreliable participants, achieving initial rejection rates of low-skill workers up to 40%. Redundancy assigns identical tasks to 3-10 workers, aggregating via majority voting or advanced models like Dawid-Skene, which jointly estimate per-worker reliability and probabilities; these have demonstrated accuracy improvements from 60% baseline to over 85% in binary labeling experiments on platforms like MTurk. systems further refine assignments by weighting past performance, with empirical tests showing sustained reliability gains in repeated tasks, though they falter against adversarial spamming. Despite these, reliability remains task-dependent: closed-ended queries rival or exceed single-expert accuracy in aggregate (e.g., crowds outperforming individuals in skin lesion diagnosis via ensemble judgments), but open-ended outputs lag, with surveys noting persistent challenges in aggregation for creative or interpretive work due to irreducible disagreement. and expert validation hybrid approaches boost metrics, as in Visual Genome annotations where crowd-expert loops yielded dense, verifiable datasets, yet scaling incurs costs 2-5 times higher than pure crowds. Empirical meta-analyses confirm that while redundancy ensures statistical robustness for verifiable tasks, unaddressed biases—like demographic skews in worker pools—can propagate systematic errors, underscoring the need for domain-specific tuning over generic optimism in platform claims.

Participation and Incentive Failures

Crowdsourcing initiatives frequently encounter low participation rates, with empirical analyses indicating that 90% of organizations soliciting external ideas receive fewer than one submission per month. This scarcity arises from inadequate crowd mobilization, as organizations often fail to adapt traditional hierarchical sourcing models to the decentralized nature of crowds, neglecting sequential engagement stages such as task definition, submission, evaluation, and feedback. High dropout rates exacerbate the issue; on platforms like , dropout levels range from 20% to 30% in tasks, even with monetary incentives and remedial measures like prewarnings or appeals to , compared to lower rates in controlled lab settings. These dropouts result in incomplete data and wasted resources, as partial compensation for non-completers risks further incentivizing withdrawals without yielding usable outputs. Incentive structures often misalign contributor motivations with organizational goals, fostering free-riding where participants exert minimal effort, anticipating acceptance of low- inputs amid high submission volumes. Winner-take-all models, common in contests, skew participation toward high-risk strategies, rendering second-place efforts valueless and discouraging broad involvement. Lack of feedback compounds this, with 88% of crowdsourcing organizations providing none to contributors, eroding trust and repeat . In open platforms, free-riders responsive to selective incentives can improve overall by countering overly optimistic peer ratings, but unchecked, they dilute collective outputs. Empirical cases illustrate these failures: Quirky, a crowdsourced product development firm, raised $185 million but collapsed in 2015 due to insufficient sustained participation and limited appeal of crowd-generated ideas. Similarly, BP's post-Deepwater Horizon solicitation yielded 100,000 ideas in 2010 but produced no actionable solutions, attributable to poor incentive alignment and rejection of crowd-favored submissions, which provoked backlash and disengagement. In complex task crowdsourcing, such as technical problem-solving, actor-specific misalignments—between contributors seeking recognition and platforms prioritizing volume—lead to fragmented efforts and outright initiative failures.

Ethical Concerns and Labor Dynamics

Crowdsourcing platforms, particularly those involving microtasks like data labeling and , have raised ethical concerns over worker exploitation due to systematically low compensation that often falls below living wages in high-cost regions. A of crowdworking remuneration revealed that microtasks typically generate an hourly wage under $6, significantly lower than comparable freelance rates, exacerbating for participants reliant on such income. This disparity stems from global labor arbitrage, where tasks are outsourced to workers in low-wage economies, but platforms headquartered in wealthier nations capture disproportionate value without providing benefits like or overtime pay. Critics argue this model undermines traditional labor regulations by classifying workers as independent contractors, evading responsibilities for enforcement or workplace safety. Labor dynamics in these ecosystems reflect power imbalances, with platforms exerting unilateral control via algorithms that assign tasks, evaluate outputs, and reject submissions without appeal, fostering worker alienation and dependency. On , for instance, automated systems commodify human effort into piece-rate payments, where requesters can impose subjective quality standards leading to unpaid revisions or bans, reducing effective earnings further. Workers, often from demographics including students, immigrants, and those in developing countries, exhibit high platform dependence due to on alternatives and the lack of portable systems, mirroring monopolistic structures that limit mobility. Empirical studies highlight how such dynamics perpetuate racialized and gendered exploitation, with tasks disproportionately assigned to underrepresented groups under opaque criteria, though platforms maintain these practices enable at low cost. Additional ethical issues encompass inadequate informed consent and privacy risks, as workers may unknowingly handle sensitive data—such as moderating violent content—without psychological support or clear disclosure of task implications. Peer-reviewed analyses emphasize the need for codes of conduct addressing rights, where contributors relinquish ownership of outputs for minimal reward, potentially enabling uncompensated innovation capture by corporations. While proponents view crowdsourcing as democratizing access to work, evidence from worker surveys indicates persistent failures in fair treatment, including proliferation mimicking legitimate tasks, which eroded trust and stability by 2024. Reforms like transparent algorithms and minimum pay floors have been proposed in academic , but adoption remains limited, sustaining debates over whether crowdsourcing constitutes a modern exploitation framework or a viable supplemental source.

Regulatory and Structural Limitations

Crowdsourcing platforms face significant regulatory hurdles stemming from the application of existing labor, , and data privacy laws, which were not designed for distributed, on-demand workforces. In the United States, workers on platforms like are classified as independent contractors under the Fair Labor Standards Act, exempting requesters from providing minimum wages, overtime, or benefits, though this has sparked misclassification lawsuits alleging violations of wage protections. For instance, in 2017, crowdsourcing provider CrowdFlower settled a class-action suit for $585,507 over claims that workers were improperly denied employee status and fair compensation. Similar disputes persist, as platforms leverage contractor status to minimize liabilities, but courts increasingly scrutinize control exerted via algorithms and task specifications, potentially reclassifying workers as employees in jurisdictions with precedents. Intellectual property regulations add complexity, as crowdsourced contributions often involve creative or inventive outputs without clear ownership chains. Contributors typically agree to broad licenses granting platforms perpetual rights, but this exposes organizers to infringement risks if submissions unknowingly replicate third-party IP, and disputes arise over moral rights or attribution in jurisdictions like the EU. Unlike traditional employment, where works-for-hire doctrines assign ownership to employers, crowdsourcing lacks standardized contracts, leading to potential invalidations if terms fail to specify joint authorship or waivers adequately. Data privacy laws impose further constraints, particularly for tasks handling personal information. Platforms must adhere to the EU's (GDPR), which mandates explicit consent, data minimization, and breach notifications, complicating anonymous task routing and exposing non-compliant operators to fines up to 4% of global revenue. In , the Consumer Privacy Act (CCPA) requires rights for data sales, challenging platforms that aggregate worker profiles for quality scoring. Crowdsourcing's decentralized nature amplifies risks of de-anonymization or unauthorized , with studies highlighting persistent gaps in worker privacy protections despite regulatory mandates. Structurally, crowdsourcing encounters inherent limits in coordination and for complex endeavors, as ad-hoc participant aggregation lacks the hierarchical oversight of firms, fostering free-riding and suboptimal task division. Research indicates that predefined workflows enhance coordination but stifle to emergent issues, increasing overhead as crowd size grows beyond simple microtasks. falters in , where untrained workers yield inconsistent outputs—evident in data annotation where error rates rise without domain expertise, limiting viability for high-stakes applications like AI training. These constraints stem from crowds' , which undermines alignment and integration compared to bounded teams, often resulting in project failures for non-routine problems.

Recent Developments and Future Outlook

Technological Integrations (AI, Blockchain)

Artificial intelligence has been integrated into crowdsourcing platforms to automate task allocation, enhance quality control, and filter unreliable contributions, addressing limitations in human-only systems. For instance, AI algorithms analyze worker performance history and task requirements to match participants more effectively, reducing errors and improving efficiency in data annotation projects. In disaster management, AI-enhanced crowdsourcing systems process real-time user-submitted for faster response, as demonstrated in a 2025 systematic review evaluating frameworks that combine with crowd inputs for . Additionally, crowdsourcing serves as a source for training AI models, with platforms distributing microtasks to global workers for labeling datasets, enabling scalable development of robust systems as seen in initiatives by organizations leveraging diverse human inputs for AI refinement. Blockchain technology introduces and transparency to crowdsourcing, mitigating issues like intermediary trust and payment disputes through smart contracts that automate rewards upon task verification. Platforms such as LaborX employ to facilitate freelance task completion with payouts, eliminating centralized gatekeepers and enabling borderless participation since its implementation. Frameworks like TFCrowd, proposed in 2021 and built on , ensure trustworthiness by using consensus mechanisms to validate contributions and prevent free-riding, with subsequent adaptations incorporating zero-knowledge proofs for privacy-preserving task execution. The zkCrowd platform, a hybrid system, balances transaction privacy with auditability in distributed crowdsourcing, supporting applications in tasks where is paramount. Integrations of AI and in crowdsourcing amplify these benefits by combining with immutable ledgers; for example, AI can pre-process crowd before blockchain verification, enhancing security in decentralized networks. In the Bank's Real-Time Prices platform, launched prior to 2025, AI aggregates crowdsourced food price across low- and middle-income countries, with blockchain potential for tamper-proof logging to further bolster reliability in economic monitoring. These advancements, evident in peer-reviewed schemes from 2023 onward, promote fairness by penalizing false reporting via cryptographic incentives, though scalability remains constrained by computational overhead in on-chain validations. The global crowdsourcing market exhibited robust growth from 2023 to 2025, reaching an estimated value of USD 50.8 billion in 2024, fueled by expanded digital infrastructure, remote tools, and corporate adoption for tasks ranging from data to challenges. Forecasts indicate a (CAGR) exceeding 36% from 2025 onward, reflecting surging demand amid economic shifts toward flexible, on-demand labor models. In the crowdsourced testing segment, critical for in software and applications, the market advanced to USD 3.18 billion in 2024, with projections for USD 3.52 billion in 2025, corresponding to a 10.7% year-over-year increase and an anticipated CAGR of 12.2% through 2030. This expansion correlates with rising complexity in mobile and web deployments, where distributed testers provide diverse device coverage unattainable through traditional in-house teams. Crowdfunding, a major crowdsourcing application for capital raising, grew from USD 19.86 billion in 2023 to USD 24.05 billion in 2024, projected to hit USD 28.44 billion in 2025, yielding a CAGR of approximately 19% over the period. These figures underscore enthusiasm for equity, reward, and donation-based models, particularly in startups and social causes, though estimates vary across reports due to differing inclusions of blockchain-integrated platforms. Crowdsourcing software and platforms, enabling task distribution and management, were valued at USD 8.3 billion in 2023, with segment-specific CAGRs of 12-15% driving incremental through 2025 amid integrations with AI for task . Microtask crowdsourcing, focused on granular , expanded from USD 283 million in 2021 to a forecasted USD 515 million by 2025, at a 16.1% CAGR, highlighting niche gains in AI training datasets. Collectively, these trends signal a market maturing beyond hype, with verifiable acceleration tied to verifiable reductions—up to 40% in testing cycles—and in global participant pools exceeding millions annually.

Emerging Risks and Opportunities

One emerging risk in crowdsourcing involves the amplification of through community-driven systems, where crowd-sourced annotations or notes can inadvertently propagate unverified claims despite mechanisms like upvoting or flagging. For instance, a 2024 study on X's found that unhelpful notes—those deemed low-quality by crowd consensus—exhibited higher readability and neutrality, potentially increasing their visibility and influence on users compared to more accurate but complex helpful notes. Similarly, platforms shifting to crowdsourced , such as Meta's 2025 pivot toward , risk elevated exposure to false content without professional oversight, as non-expert crowds may prioritize consensus over empirical verification. This vulnerability stems from crowds' susceptibility to and echo chambers, particularly in high-stakes domains like or elections, where collaborative groups outperformed individuals in detection but still faltered against sophisticated . Privacy and data security pose another escalating concern, especially in crowdsourced data annotation for AI training, where tasks involving sensitive information are distributed to anonymous workers, heightening breach risks. A analysis highlighted that exposing critical datasets to broad worker pools without robust controls can lead to unauthorized access or leaks, as seen in platforms where task publication bypasses stringent vetting. Compliance with regulations like GDPR becomes challenging amid these distributed workflows, with real-time monitoring systems proposed as mitigations but not yet widely adopted by mid-2025. In cybersecurity contexts, crowdsourced vulnerability hunting introduces hybrid threats, where malicious actors exploit open calls to probe systems under the guise of ethical testing. Opportunities arise from hybrid integrations with AI and blockchain, enabling more scalable and verifiable crowdsourcing models. AI-augmented systems, projected to streamline workflows by 2030, allow crowds to handle complex tasks like synthetic media verification, where human oversight complements to filter deepfakes more effectively than pure . Blockchain facilitates decentralized incentive structures, reducing fraud via transparent ledgers for contributions, as evidenced by emerging platforms combining it with crowdsourcing for secure data in AI datasets since 2023. In cyber defense, crowdsourced threat intelligence sharing—while privacy-protected—has gained traction, with 2025 frameworks emphasizing Protocols to enable rapid, collective responses to attacks without full disclosure. These advancements could expand crowdsourcing into applications, leveraging diverse global inputs for real-time hybrid threat mitigation.

References

  1. https://www.[investopedia](/page/Investopedia).com/terms/c/crowdsourcing.asp
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