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Explainable artificial intelligence
Explainable artificial intelligence
Explainable artificial intelligence
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Artificial intelligence (AI)
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Within artificial intelligence (AI), explainable AI (XAI), generally overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans with the ability of intellectual oversight over AI algorithms.[1][2] The main focus is on the reasoning behind the decisions or predictions made by the AI algorithms,[3] to make them more understandable and transparent.[4] This addresses users' requirement to assess safety and scrutinize the automated decision making in applications.[5] XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.[6][7]

XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason.[8] XAI may be an implementation of the social right to explanation.[9] Even if there is no such legal right or regulatory requirement, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions.[10] XAI aims to explain what has been done, what is being done, and what will be done next, and to unveil which information these actions are based on.[11] This makes it possible to confirm existing knowledge, challenge existing knowledge, and generate new assumptions.[12]

Background

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Machine learning (ML) algorithms used in AI can be categorized as white-box or black-box.[13] White-box models provide results that are understandable to experts in the domain. Black-box models, on the other hand, are extremely hard to explain and may not be understood even by domain experts.[14] XAI algorithms follow the three principles of transparency, interpretability, and explainability.

  • A model is transparent "if the processes that extract model parameters from training data and generate labels from testing data can be described and motivated by the approach designer."[15]
  • Interpretability describes the possibility of comprehending the ML model and presenting the underlying basis for decision-making in a way that is understandable to humans.[16][17][18]
  • Explainability is a concept that is recognized as important, but a consensus definition is not yet available;[15] one possibility is "the collection of features of the interpretable domain that have contributed, for a given example, to producing a decision (e.g., classification or regression)".[19]

In summary, Interpretability refers to the user's ability to understand model outputs, while Model Transparency includes Simulatability (reproducibility of predictions), Decomposability (intuitive explanations for parameters), and Algorithmic Transparency (explaining how algorithms work). Model Functionality focuses on textual descriptions, visualization, and local explanations, which clarify specific outputs or instances rather than entire models. All these concepts aim to enhance the comprehensibility and usability of AI systems.[20] If algorithms fulfill these principles, they provide a basis for justifying decisions, tracking them and thereby verifying them, improving the algorithms, and exploring new facts.[21]

Sometimes it is also possible to achieve a high-accuracy result with white-box ML algorithms. These algorithms have an interpretable structure that can be used to explain predictions.[22] Concept Bottleneck Models, which use concept-level abstractions to explain model reasoning, are examples of this and can be applied in both image[23] and text[24] prediction tasks. This is especially important in domains like medicine, defense, finance, and law, where it is crucial to understand decisions and build trust in the algorithms.[11] Many researchers argue that, at least for supervised machine learning, the way forward is symbolic regression, where the algorithm searches the space of mathematical expressions to find the model that best fits a given dataset.[25][26][27]

AI systems optimize behavior to satisfy a mathematically specified goal system chosen by the system designers, such as the command "maximize the accuracy of assessing how positive film reviews are in the test dataset." The AI may learn useful general rules from the test set, such as "reviews containing the word "horrible" are likely to be negative." However, it may also learn inappropriate rules, such as "reviews containing 'Daniel Day-Lewis' are usually positive"; such rules may be undesirable if they are likely to fail to generalize outside the training set, or if people consider the rule to be "cheating" or "unfair." A human can audit rules in an XAI to get an idea of how likely the system is to generalize to future real-world data outside the test set.[28]

Goals

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Cooperation between agents – in this case, algorithms and humans – depends on trust. If humans are to accept algorithmic prescriptions, they need to trust them. Incompleteness in formal trust criteria is a barrier to optimization. Transparency, interpretability, and explainability are intermediate goals on the road to these more comprehensive trust criteria.[29] This is particularly relevant in medicine,[30] especially with clinical decision support systems (CDSS), in which medical professionals should be able to understand how and why a machine-based decision was made in order to trust the decision and augment their decision-making process.[31]

AI systems sometimes learn undesirable tricks that do an optimal job of satisfying explicit pre-programmed goals on the training data but do not reflect the more nuanced implicit desires of the human system designers or the full complexity of the domain data. For example, a 2017 system tasked with image recognition learned to "cheat" by looking for a copyright tag that happened to be associated with horse pictures rather than learning how to tell if a horse was actually pictured.[7] In another 2017 system, a supervised learning AI tasked with grasping items in a virtual world learned to cheat by placing its manipulator between the object and the viewer in a way such that it falsely appeared to be grasping the object.[32][33]

One transparency project, the DARPA XAI program, aims to produce "glass box" models that are explainable to a "human-in-the-loop" without greatly sacrificing AI performance. Human users of such a system can understand the AI's cognition (both in real-time and after the fact) and can determine whether to trust the AI.[34] Other applications of XAI are knowledge extraction from black-box models and model comparisons.[35] In the context of monitoring systems for ethical and socio-legal compliance, the term "glass box" is commonly used to refer to tools that track the inputs and outputs of the system in question, and provide value-based explanations for their behavior. These tools aim to ensure that the system operates in accordance with ethical and legal standards, and that its decision-making processes are transparent and accountable. The term "glass box" is often used in contrast to "black box" systems, which lack transparency and can be more difficult to monitor and regulate.[36] The term is also used to name a voice assistant that produces counterfactual statements as explanations.[37]

Explainability and interpretability techniques

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There is a subtle difference between the terms explainability and interpretability in the context of AI.[38]

Term Definition Source
Interpretability "level of understanding how the underlying (AI) technology works" ISO/IEC TR 29119-11:2020(en), 3.1.42[39]
Explainability "level of understanding how the AI-based system ... came up with a given result" ISO/IEC TR 29119-11:2020(en), 3.1.31[39]

Some explainability techniques don't involve understanding how the model works, and may work across various AI systems. Treating the model as a black box and analyzing how marginal changes to the inputs affect the result sometimes provides a sufficient explanation.

Explainability

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Explainability is useful for ensuring that AI models are not making decisions based on irrelevant or otherwise unfair criteria. For classification and regression models, several popular techniques exist:

  • Partial dependency plots show the marginal effect of an input feature on the predicted outcome.
  • SHAP (SHapley Additive exPlanations) enables visualization of the contribution of each input feature to the output. It works by calculating Shapley values, which measure the average marginal contribution of a feature across all possible combinations of features.[40]
  • Feature importance estimates how important a feature is for the model. It is usually done using permutation importance, which measures the performance decrease when it the feature value randomly shuffled across all samples.
  • LIME (Local Interpretable Model-Agnostic Explanations method) approximates locally a model's outputs with a simpler, interpretable model.[41]
  • Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned.[42]

For images, saliency maps highlight the parts of an image that most influenced the result.[43]

Systems that are expert or knowledge based are software systems that are made by experts. This system consists of a knowledge based encoding for the domain knowledge. This system is usually modeled as production rules, and someone uses this knowledge base which the user can question the system for knowledge. In expert systems, the language and explanations are understood with an explanation for the reasoning or a problem solving activity.[5]

However, these techniques are not very suitable for language models like generative pretrained transformers. Since these models generate language, they can provide an explanation, but which may not be reliable. Other techniques include attention analysis (examining how the model focuses on different parts of the input), probing methods (testing what information is captured in the model's representations), causal tracing (tracing the flow of information through the model) and circuit discovery (identifying specific subnetworks responsible for certain behaviors). Explainability research in this area overlaps significantly with interpretability and alignment research.[44]

Interpretability

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Grokking is an example of phenomenon studied in interpretability. It involves a model that initially memorizes all the answers (overfitting), but later adopts an algorithm that generalizes to unseen data.[45]

Scholars sometimes use the term "mechanistic interpretability" to refer to the process of reverse-engineering artificial neural networks to understand their internal decision-making mechanisms and components, similar to how one might analyze a complex machine or computer program.[46]

Interpretability research often focuses on generative pretrained transformers. It is particularly relevant for AI safety and alignment, as it may enable to identify signs of undesired behaviors such as sycophancy, deceptiveness or bias, and to better steer AI models.[47]

Studying the interpretability of the most advanced foundation models often involves searching for an automated way to identify "features" in generative pretrained transformers. In a neural network, a feature is a pattern of neuron activations that corresponds to a concept. A compute-intensive technique called "dictionary learning" makes it possible to identify features to some degree. Enhancing the ability to identify and edit features is expected to significantly improve the safety of frontier AI models.[48][49]

For convolutional neural networks, DeepDream can generate images that strongly activate a particular neuron, providing a visual hint about what the neuron is trained to identify.[50]

History and methods

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During the 1970s to 1990s, symbolic reasoning systems, such as MYCIN,[51] GUIDON,[52] SOPHIE,[53] and PROTOS[54][55] could represent, reason about, and explain their reasoning for diagnostic, instructional, or machine-learning (explanation-based learning) purposes. MYCIN, developed in the early 1970s as a research prototype for diagnosing bacteremia infections of the bloodstream, could explain[56] which of its hand-coded rules contributed to a diagnosis in a specific case. Research in intelligent tutoring systems resulted in developing systems such as SOPHIE that could act as an "articulate expert", explaining problem-solving strategy at a level the student could understand, so they would know what action to take next. For instance, SOPHIE could explain the qualitative reasoning behind its electronics troubleshooting, even though it ultimately relied on the SPICE circuit simulator. Similarly, GUIDON added tutorial rules to supplement MYCIN's domain-level rules so it could explain the strategy for medical diagnosis. Symbolic approaches to machine learning relying on explanation-based learning, such as PROTOS, made use of explicit representations of explanations expressed in a dedicated explanation language, both to explain their actions and to acquire new knowledge.[55]

In the 1980s through the early 1990s, truth maintenance systems (TMS) extended the capabilities of causal-reasoning, rule-based, and logic-based inference systems.[57]: 360–362  A TMS explicitly tracks alternate lines of reasoning, justifications for conclusions, and lines of reasoning that lead to contradictions, allowing future reasoning to avoid these dead ends. To provide an explanation, they trace reasoning from conclusions to assumptions through rule operations or logical inferences, allowing explanations to be generated from the reasoning traces. As an example, consider a rule-based problem solver with just a few rules about Socrates that concludes he has died from poison:

By just tracing through the dependency structure the problem solver can construct the following explanation: "Socrates died because he was mortal and drank poison, and all mortals die when they drink poison. Socrates was mortal because he was a man and all men are mortal. Socrates drank poison because he held dissident beliefs, the government was conservative, and those holding conservative dissident beliefs under conservative governments must drink poison."[58]: 164–165 

By the 1990s researchers began studying whether it is possible to meaningfully extract the non-hand-coded rules being generated by opaque trained neural networks.[59] Researchers in clinical expert systems creating[clarification needed] neural network-powered decision support for clinicians sought to develop dynamic explanations that allow these technologies to be more trusted and trustworthy in practice.[9] In the 2010s public concerns about racial and other bias in the use of AI for criminal sentencing decisions and findings of creditworthiness may have led to increased demand for transparent artificial intelligence.[7] As a result, many academics and organizations are developing tools to help detect bias in their systems.[60]

Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI.[61]

Explainable AI has been recently a new topic researched amongst the context of modern deep learning. Modern complex AI techniques, such as deep learning, are naturally opaque.[62] To address this issue, methods have been developed to make new models more explainable and interpretable.[63][17][16][64][65][66] This includes layerwise relevance propagation (LRP), a technique for determining which features in a particular input vector contribute most strongly to a neural network's output.[67][68] Other techniques explain some particular prediction made by a (nonlinear) black-box model, a goal referred to as "local interpretability".[69][70][71][72][73][74] We still today cannot explain the output of today's DNNs without the new explanatory mechanisms, we also can't by the neural network, or external explanatory components [75] There is also research on whether the concepts of local interpretability can be applied to a remote context, where a model is operated by a third-party.[76][77]

There has been work on making glass-box models which are more transparent to inspection.[22][78] This includes decision trees,[79] Bayesian networks, sparse linear models,[80] and more.[81] The Association for Computing Machinery Conference on Fairness, Accountability, and Transparency (ACM FAccT) was established in 2018 to study transparency and explainability in the context of socio-technical systems, many of which include artificial intelligence.[82][83]

Some techniques allow visualisations of the inputs to which individual software neurons respond to most strongly. Several groups found that neurons can be aggregated into circuits that perform human-comprehensible functions, some of which reliably arise across different networks trained independently.[84][85]

There are various techniques to extract compressed representations of the features of given inputs, which can then be analysed by standard clustering techniques. Alternatively, networks can be trained to output linguistic explanations of their behaviour, which are then directly human-interpretable.[86] Model behaviour can also be explained with reference to training data—for example, by evaluating which training inputs influenced a given behaviour the most,[87] or by approximating its predictions using the most similar instances from the training data.[88]

The use of explainable artificial intelligence (XAI) in pain research, specifically in understanding the role of electrodermal activity for automated pain recognition: hand-crafted features and deep learning models in pain recognition, highlighting the insights that simple hand-crafted features can yield comparative performances to deep learning models and that both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data.[89]

Regulation

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As regulators, official bodies, and general users come to depend on AI-based dynamic systems, clearer accountability will be required for automated decision-making processes to ensure trust and transparency. The first global conference exclusively dedicated to this emerging discipline was the 2017 International Joint Conference on Artificial Intelligence: Workshop on Explainable Artificial Intelligence (XAI).[90] It has evolved over the years, with various workshops organised and co-located to many other international conferences, and it has now a dedicated global event, "The world conference on eXplainable Artificial Intelligence", with its own proceedings.[91][92]

The European Union introduced a right to explanation in the General Data Protection Regulation (GDPR) to address potential problems stemming from the rising importance of algorithms. The implementation of the regulation began in 2018. However, the right to explanation in GDPR covers only the local aspect of interpretability. In the United States, insurance companies are required to be able to explain their rate and coverage decisions.[93] In France the Loi pour une République numérique (Digital Republic Act) grants subjects the right to request and receive information pertaining to the implementation of algorithms that process data about them.

Limitations

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Despite ongoing endeavors to enhance the explainability of AI models, they persist with several inherent limitations.

Adversarial parties

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By making an AI system more explainable, we also reveal more of its inner workings. For example, the explainability method of feature importance identifies features or variables that are most important in determining the model's output, while the influential samples method identifies the training samples that are most influential in determining the output, given a particular input.[94] Adversarial parties could take advantage of this knowledge.

For example, competitor firms could replicate aspects of the original AI system in their own product, thus reducing competitive advantage.[95] An explainable AI system is also susceptible to being "gamed"—influenced in a way that undermines its intended purpose. One study gives the example of a predictive policing system; in this case, those who could potentially "game" the system are the criminals subject to the system's decisions. In this study, developers of the system discussed the issue of criminal gangs looking to illegally obtain passports, and they expressed concerns that, if given an idea of what factors might trigger an alert in the passport application process, those gangs would be able to "send guinea pigs" to test those triggers, eventually finding a loophole that would allow them to "reliably get passports from under the noses of the authorities".[96]

Adaptive integration and explanation

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Many approaches that it uses provides explanation in general, it doesn't take account for the diverse backgrounds and knowledge level of the users. This leads to challenges with accurate comprehension for all users. Expert users can find the explanations lacking in depth, and are oversimplified, while a beginner user may struggle understanding the explanations as they are complex. This limitation downplays the ability of the XAI techniques to appeal to their users with different levels of knowledge, which can impact the trust from users and who uses it. The quality of explanations can be different amongst their users as they all have different expertise levels, including different situation and conditions.[97]

Technical complexity

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A fundamental barrier to making AI systems explainable is the technical complexity of such systems. End users often lack the coding knowledge required to understand software of any kind. Current methods used to explain AI are mainly technical ones, geared toward machine learning engineers for debugging purposes, rather than toward the end users who are ultimately affected by the system, causing "a gap between explainability in practice and the goal of transparency".[94] Proposed solutions to address the issue of technical complexity include either promoting the coding education of the general public so technical explanations would be more accessible to end users, or providing explanations in layperson terms.[95]

The solution must avoid oversimplification. It is important to strike a balance between accuracy – how faithfully the explanation reflects the process of the AI system – and explainability – how well end users understand the process. This is a difficult balance to strike, since the complexity of machine learning makes it difficult for even ML engineers to fully understand, let alone non-experts.[94]

Understanding versus trust

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The goal of explainability to end users of AI systems is to increase trust in the systems, even "address concerns about lack of 'fairness' and discriminatory effects".[95] However, even with a good understanding of an AI system, end users may not necessarily trust the system.[98] In one study, participants were presented with combinations of white-box and black-box explanations, and static and interactive explanations of AI systems. While these explanations served to increase both their self-reported and objective understanding, it had no impact on their level of trust, which remained skeptical.[99]

This outcome was especially true for decisions that impacted the end user in a significant way, such as graduate school admissions. Participants judged algorithms to be too inflexible and unforgiving in comparison to human decision-makers; instead of rigidly adhering to a set of rules, humans are able to consider exceptional cases as well as appeals to their initial decision.[99] For such decisions, explainability will not necessarily cause end users to accept the use of decision-making algorithms. We will need to either turn to another method to increase trust and acceptance of decision-making algorithms, or question the need to rely solely on AI for such impactful decisions in the first place.

However, some emphasize that the purpose of explainability of artificial intelligence is not to merely increase users' trust in the system's decisions, but to calibrate the users' level of trust to the correct level.[100] According to this principle, too much or too little user trust in the AI system will harm the overall performance of the human-system unit. When the trust is excessive, the users are not critical of possible mistakes of the system and when the users do not have enough trust in the system, they will not exhaust the benefits inherent in it.

Criticism

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Some scholars have suggested that explainability in AI should be considered a goal secondary to AI effectiveness, and that encouraging the exclusive development of XAI may limit the functionality of AI more broadly.[101][102] Critiques of XAI rely on developed concepts of mechanistic and empiric reasoning from evidence-based medicine to suggest that AI technologies can be clinically validated even when their function cannot be understood by their operators.[101]

Some researchers advocate the use of inherently interpretable machine learning models, rather than using post-hoc explanations in which a second model is created to explain the first. This is partly because post-hoc models increase the complexity in a decision pathway and partly because it is often unclear how faithfully a post-hoc explanation can mimic the computations of an entirely separate model.[22] However, another view is that what is important is that the explanation accomplishes the given task at hand, and whether it is pre or post-hoc doesn't matter. If a post-hoc explanation method helps a doctor diagnose cancer better, it is of secondary importance whether it is a correct/incorrect explanation.

The goals of XAI amount to a form of lossy compression that will become less effective as AI models grow in their number of parameters. Along with other factors this leads to a theoretical limit for explainability.[103]

Explainability in social choice

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Explainability was studied also in social choice theory. Social choice theory aims at finding solutions to social decision problems, that are based on well-established axioms. Ariel D. Procaccia[104] explains that these axioms can be used to construct convincing explanations to the solutions. This principle has been used to construct explanations in various subfields of social choice.

Voting

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Cailloux and Endriss[105] present a method for explaining voting rules using the axioms that characterize them. They exemplify their method on the Borda voting rule .

Peters, Procaccia, Psomas and Zhou[106] present an algorithm for explaining the outcomes of the Borda rule using O(m2) explanations, and prove that this is tight in the worst case.

Participatory budgeting

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Yang, Hausladen, Peters, Pournaras, Fricker and Helbing[107] present an empirical study of explainability in participatory budgeting. They compared the greedy and the equal shares rules, and three types of explanations: mechanism explanation (a general explanation of how the aggregation rule works given the voting input), individual explanation (explaining how many voters had at least one approved project, at least 10000 CHF in approved projects), and group explanation (explaining how the budget is distributed among the districts and topics). They compared the perceived trustworthiness and fairness of greedy and equal shares, before and after the explanations. They found out that, for MES, mechanism explanation yields the highest increase in perceived fairness and trustworthiness; the second-highest was Group explanation. For Greedy, Mechanism explanation increases perceived trustworthiness but not fairness, whereas Individual explanation increases both perceived fairness and trustworthiness. Group explanation decreases the perceived fairness and trustworthiness.

Payoff allocation

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Nizri, Azaria and Hazon[108] present an algorithm for computing explanations for the Shapley value. Given a coalitional game, their algorithm decomposes it to sub-games, for which it is easy to generate verbal explanations based on the axioms characterizing the Shapley value. The payoff allocation for each sub-game is perceived as fair, so the Shapley-based payoff allocation for the given game should seem fair as well. An experiment with 210 human subjects shows that, with their automatically generated explanations, subjects perceive Shapley-based payoff allocation as significantly fairer than with a general standard explanation.

See also

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References

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Explainable artificial intelligence

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Explainable artificial intelligence (XAI) is a subfield of that develops techniques to render the predictions, decisions, and internal workings of AI models comprehensible to human users, countering the opacity inherent in complex systems like deep neural networks. This field addresses the fundamental trade-off in contemporary between high predictive accuracy and interpretability: "black-box" models achieve superior performance yet obscure their causal mechanisms, which impedes trust, , and deployment in high-stakes domains such as , , and autonomous systems. Prominent approaches encompass intrinsically interpretable models (e.g., linear regressions or decision trees that expose decision rules directly) and post-hoc explanation methods (e.g., feature attribution techniques like SHAP values, which quantify input contributions to outputs, or local surrogates like LIME that approximate model behavior around specific instances). Achievements include enhanced regulatory adherence under frameworks like the EU AI Act, improved model robustness through interpretability-driven refinements, and empirical validations in sectors like healthcare where XAI aids clinicians in verifying diagnostic rationales. Yet controversies endure: critics argue many XAI tools yield superficial or misleading proxies rather than genuine causal insights into model reasoning, potentially fostering overconfidence in flawed systems, while debates rage over whether scalable explanations for nonlinear are fundamentally unattainable without sacrificing performance.

Definitions and Fundamentals

Core Concepts and Distinctions

Explainable artificial intelligence (XAI) encompasses techniques designed to elucidate the processes of models, addressing the opacity inherent in many high-performance algorithms. Central to XAI are distinctions between model types and explanation scopes, which inform the choice of interpretability methods. Black-box models, such as deep neural networks, exhibit complex internal structures where input-output mappings are not directly observable, limiting human comprehension of causal pathways. In contrast, white-box models, including or decision trees, feature transparent architectures that allow direct inspection of feature contributions and decision rules. This dichotomy highlights a performance : black-box models often achieve superior predictive accuracy on intricate datasets, while white-box models prioritize inherent understandability at potential cost to precision. Explanations in XAI further divide into intrinsic (ante-hoc) and post-hoc categories. Intrinsic explanations arise from models designed for interpretability from , where the algorithm's logic—such as rule-based splits in decision trees—naturally reveals feature importance and prediction rationale without additional processing. Post-hoc explanations, conversely, apply to trained models regardless of complexity, generating approximations or surrogates to probe behavior; examples include feature perturbation methods like LIME, which localize explanations around specific instances. Post-hoc approaches enable flexibility for black-box systems but risk fidelity issues, as surrogate models may not perfectly capture the original's nuances. Explanations also vary by scope: local versus global. Local explanations target individual predictions, attributing outcomes to feature values for a single input, as in SHAP values that decompose a prediction's deviation from baseline. Global explanations, by comparison, aggregate insights across the dataset to describe overall model tendencies, such as average feature impacts or decision boundaries, aiding in bias detection or generalization assessment. These scopes are not mutually exclusive; hybrid methods increasingly combine them for comprehensive diagnostics. Overlapping terms like transparency, interpretability, and explainability lack universal formalization, complicating XAI . Transparency typically denotes openness of model components and flows, interpretability the ease of discerning decision causes, and explainability the provision of human-readable rationales—yet usages vary across literature, with explainability often encompassing post-hoc tools for non-interpretable systems. This conceptual fluidity underscores the field's emphasis on context-specific utility over rigid .

Taxonomy of Explainability Approaches

Explainability approaches in are classified along multiple dimensions to capture their design, applicability, and output characteristics, as surveyed in recent literature. A core distinction lies between intrinsic (ante-hoc) methods, which employ models designed to be interpretable from the outset—such as linear models, decision trees, or rule-based systems—and post-hoc methods, which generate explanations for opaque "black-box" models after training, including techniques like surrogate models or attribution methods. This addresses the between model performance and transparency, with intrinsic approaches prioritizing at potential cost to accuracy on complex tasks. Another fundamental axis is scope: local explanations focus on individual predictions or instances, elucidating why a specific input yields a particular output (e.g., via Local Interpretable Model-agnostic Explanations (LIME), which approximates a black-box locally with a simple model), whereas global explanations describe the model's overall behavior across the input space, such as through feature importance rankings or partial dependence plots. Local methods dominate for debugging single cases, as evidenced by their prevalence in applications like medical diagnostics, while global methods aid in auditing systemic biases or regulatory compliance. Methods are further differentiated by applicability: model-specific techniques leverage the internal structure of particular architectures, such as Layer-wise Relevance Propagation (LRP) for neural networks, which decomposes predictions via of relevance scores, or saliency maps that highlight gradient-based sensitivities in convolutional layers. In contrast, model-agnostic approaches, like SHapley Additive exPlanations (SHAP), apply universally by treating models as oracles and using game-theoretic values to assign feature contributions, enabling portability across algorithms but often at higher computational expense. Taxonomies also categorize by methodology or functioning, encompassing perturbation-based techniques that probe inputs (e.g., LIME's sampling around instances or counterfactual , which identifies minimal changes to alter outcomes), gradient-based methods reliant on differentiability (e.g., Integrated Gradients, which accumulate gradients along a baseline-to-input path for stable attributions), and others like mechanisms in transformers or example-based retrievals. Output forms vary correspondingly, from visualizations (heatmaps, decision paths) to textual rules or prototypes, with selection guided by domain needs—e.g., rule extraction for legal interpretability. These dimensions often intersect, yielding hybrid classifications; for instance, SHAP can be local and post-hoc yet adaptable globally via kernel approximations. Challenges in unification persist due to overlapping terms and context-dependent validity, as no single fully resolves ambiguities like the fidelity-interpretability , prompting ongoing refinements in surveys up to 2024. Empirical validation remains sparse, with many methods evaluated via proxy metrics rather than real-world causal impacts.

Motivations and Objectives

Technical and Practical Drivers

Technical drivers for explainable artificial intelligence (XAI) primarily stem from the need to diagnose and enhance the internal workings of complex models, particularly black-box systems like deep neural networks, where opacity hinders identification of errors or inefficiencies. Explanations enable developers to pinpoint failure modes, such as reliance on spurious correlations in training data, facilitating targeted that improves and robustness. For instance, XAI techniques like feature attribution methods reveal how models weigh inputs, allowing iterative refinements that address biases or without retraining from scratch. Empirical evidence underscores these benefits: in controlled studies, integrating XAI into model development pipelines has yielded accuracy gains of 15% to 30% by exposing and mitigating flawed decision pathways, as observed in platforms designed for iterative AI refinement. Moreover, XAI supports optimization by quantifying the impact of hyperparameters or architectural changes on predictions, bridging the gap between high-level metrics like accuracy and causal mechanisms underlying model behavior. This is particularly vital for tasks, where transparency aids in validating assumptions about data distributions and prevents degradation in deployment scenarios differing from training environments. Practical drivers arise from deployment imperatives in regulated or high-stakes domains, where unexplained decisions impede and integration with oversight. In industries like and healthcare, XAI ensures traceability for auditing approvals or diagnostic recommendations, reducing liability risks by clarifying AI contributions to outcomes. Regulatory frameworks amplify this: the European Union's AI Act, effective from August 2024 with phased enforcement through 2027, mandates transparency and explainability for high-risk systems, requiring providers to disclose decision logic to avoid prohibited opacity in areas like credit scoring or medical devices. Beyond compliance, practical addresses end-user trust and ; for autonomous driving, XAI elucidates real-time rationales, enabling engineers to intervene in edge cases and regulators to verify claims. Industry reports highlight that without explanations, AI deployment stalls due to from stakeholders, whereas interpretable outputs foster by aligning machine reasoning with verifiable human , as seen in cybersecurity applications where XAI unpacks intrusion detection to preempt false positives. These drivers collectively prioritize causal insight over mere predictive power, ensuring AI systems scale reliably in production environments.

Ethical and Societal Rationales

The push for explainable artificial intelligence (XAI) stems from ethical imperatives to ensure accountability in AI-driven decisions, particularly where opaque "black-box" models obscure the causal pathways leading to outcomes that affect human lives. In high-stakes domains such as healthcare and , unexplainable models hinder the ability to decisions for errors or unintended harms, making it challenging to hold developers, deployers, or users responsible for discriminatory or unjust results. For instance, black-box systems in or loan approvals have been empirically linked to perpetuating societal biases embedded in training data, as decisions cannot be readily traced to specific inputs or algorithmic logic, exacerbating inequalities without recourse for affected individuals. XAI techniques, by contrast, facilitate post-hoc scrutiny to identify and mitigate such biases, aligning AI outputs more closely with ethical standards of fairness and non-discrimination. Societally, the opacity of advanced AI models erodes , as users and regulators lack verifiable insight into how systems process or prioritize factors, fostering skepticism toward widespread adoption in like autonomous vehicles or medical diagnostics. Empirical studies indicate that explainability enhances perceived trustworthiness by allowing stakeholders to validate decision rationales against real-world expectations, thereby supporting broader societal acceptance and reducing risks of misuse or over-reliance on unverified predictions. This is particularly salient in regulatory contexts, where transparent AI enables oversight bodies to enforce compliance with legal norms, such as detecting unfair representations that under- or over-represent demographic groups, which could otherwise amplify minority biases at scale. However, while XAI promotes these goals, it does not inherently guarantee fairness, as interpretable models can still encode biased logic if not rigorously vetted, underscoring the need for complementary empirical validation beyond mere transparency. From a first-principles perspective, ethical rationales for XAI emphasize causal realism: understanding the mechanistic "why" behind predictions counters the pitfalls of correlational black-box outputs, which may mimic without genuine alignment to human values or verifiable . This is evidenced in frameworks advocating XAI integration throughout the AI lifecycle to embed responsibility, where explainability tools aid in auditing for ethical alignment, such as ensuring decisions in prioritize equitable outcomes over opaque efficiency gains. Societally, such approaches mitigate risks of democratic , as unexplainable AI in or advising could entrench power imbalances by shielding influential actors from scrutiny, whereas explainable variants empower informed and calibration based on auditable evidence. Overall, these rationales drive XAI not as a but as a necessary safeguard against the societal costs of deploying powerful yet inscrutable systems, with ongoing quantifying improvements in metrics like detection rates in controlled deployments.

Relation to AI Safety and Reliability

Explainable artificial intelligence (XAI) contributes to by enabling the detection of biases, failures, and unintended behaviors in models, allowing developers to processes and mitigate risks before deployment. For instance, XAI techniques facilitate the identification of model vulnerabilities, such as discriminatory patterns in predictive algorithms, which could otherwise lead to harmful outcomes in high-stakes applications like healthcare or autonomous systems. This transparency supports proactive safety measures, including the validation of model fairness and the correction of erroneous predictions, thereby reducing the potential for systemic errors or adversarial exploits. In the context of AI alignment—ensuring systems pursue intended objectives without deviation—XAI, particularly through mechanistic interpretability, provides insights into internal representations and causal pathways within neural networks, aiding efforts to verify goal-directed behavior. Researchers argue that such interpretability is essential for scaling oversight of advanced models, as it allows humans to probe for misaligned incentives or emergent capabilities that opaque "black-box" systems obscure. However, limitations exist; interpretability methods may fail to reliably detect sophisticated in trained models, where deceptive alignments could evade superficial explanations, underscoring that XAI is a necessary but insufficient tool for comprehensive guarantees. Regarding reliability, XAI enhances system dependability by supporting and empirical validation of model robustness against distributional shifts or adversarial inputs, fostering verifiable in real-world scenarios. Techniques like post-hoc explanations and surrogate models enable stakeholders to assess consistency and generalize predictions, which is critical for domains requiring high assurance, such as safety-critical . Empirical studies demonstrate that integrating XAI improves fault detection rates, with interpretable components reducing downtime in deployed systems by clarifying failure modes. Despite these benefits, over-reliance on explanations risks a false of if metrics for explainability lack rigorous grounding, potentially masking underlying unreliability in complex models.

Historical Evolution

Pre-2010 Foundations in Interpretable Machine Learning

The foundations of interpretable machine learning prior to 2010 were rooted in and statistical modeling traditions that emphasized transparency through explicit rules and simple structures. Expert systems, prominent from the 1970s to the 1980s, relied on human-engineered knowledge bases of production rules and logical inference, enabling explanations via traces of reasoning steps, such as forward or . A seminal example was , developed in the 1970s and formalized in 1984, which diagnosed bacterial infections using approximately 450 rules and provided justifications for recommendations by citing evidential rules and confidence factors. These systems prioritized comprehensibility for domain experts, though they suffered from bottlenecks and limited to complex, data-driven domains. In parallel, statistical advanced inherently interpretable models like and generalized linear models, where parameter coefficients directly quantified feature contributions to predictions, facilitating causal and predictive insights since the early but gaining ML prominence in the . Decision trees emerged as a cornerstone for and regression tasks, offering visual tree structures that traced decision paths from root to leaf nodes, thus providing global interpretability. Leo Breiman and colleagues introduced Classification and Regression Trees (CART) in 1984, employing with Gini impurity or criteria to build trees amenable to for and . J. Ross Quinlan's (1986) and subsequent C4.5 (1993) further refined this by using information gain from entropy to select splits, enabling rule extraction from trees for propositional logic representations. These methods balanced predictive accuracy with human-readable hierarchies, influencing applications in fields like and where decision rationale was essential. As neural networks gained traction in the late and following backpropagation's popularization, their black-box nature prompted early post-hoc interpretability efforts to approximate or decompose complex models. Techniques included , which measured output changes to input perturbations, and visualization of hidden unit activations to infer learned representations. Rule extraction methods treated neural networks as oracles, distilling them into surrogate decision trees or lists; for instance, Andrews et al. (1995) proposed decompositional and pedagogical approaches to derive symbolic rules from trained connectionist systems, evaluating via accuracy preservation. Craven and Shavlik's Trepan (1996) extended this by querying neural networks to induce oblique decision trees, prioritizing to the original model over pedagogical simplicity. These foundations underscored a between model complexity and interpretability, favoring simpler, transparent alternatives unless post-hoc surrogates could reliably bridge the gap, as evidenced in domains requiring or error tracing.

2010s Revival and DARPA's Role

The resurgence of interest in explainable artificial intelligence during the 2010s was driven by the rapid adoption of deep learning models, which achieved state-of-the-art performance in tasks such as image recognition and natural language processing but operated as opaque "black boxes," complicating trust and accountability in high-stakes applications like autonomous systems and decision support. This shift contrasted with earlier emphases on inherently interpretable models, as the predictive power of neural networks—exemplified by AlexNet's 2012 ImageNet victory with an error rate of 15.3% versus prior bests over 25%—prioritized accuracy over transparency, prompting renewed focus on methods to elucidate model internals without sacrificing capability. Early 2010s publications, such as those exploring feature visualization in convolutional networks, laid groundwork, but systematic efforts coalesced mid-decade amid growing deployment in defense and healthcare domains where erroneous decisions could yield catastrophic outcomes. The U.S. Defense Advanced Research Projects Agency () catalyzed this revival through its Explainable Artificial Intelligence (XAI) program, formulated in 2015 to develop techniques enabling humans to comprehend, trust, and effectively manage AI outputs in operational contexts. Launched with initial funding announcements in 2016 and broader solicitations by 2017, the program allocated approximately $50 million across , applied development, and evaluation thrust areas, targeting both local explanations (e.g., for individual predictions) and global model behaviors. DARPA program manager David Gunning emphasized creating "glass box" models compatible with oversight, particularly for applications like tactical decision aids, where unexplained AI recommendations risked mission failure or ethical lapses. DARPA's XAI initiative influenced broader academia and industry by funding over 20 performers, including universities and firms like and , to prototype tools such as scalable visualizations and hybrids that preserved performance—e.g., achieving explanation fidelity scores above 90% in benchmark tests—while advancing standards for user-centric validation. Retrospective analyses credit the program with shifting XAI from ad-hoc techniques to rigorous engineering, though challenges persisted in scaling explanations for non-expert end-users and verifying causal validity beyond correlative patterns. By program's end around 2021, it had spurred open-source libraries and interdisciplinary collaborations, embedding explainability as a core requirement in subsequent AI governance frameworks.

2020s Developments and Integration with Deep Learning

The marked a pivotal shift in explainable artificial intelligence (XAI) toward deeper integration with architectures, driven by the dominance of transformer-based models in large language models (LLMs). Researchers increasingly focused on mechanistic interpretability, aiming to reverse-engineer internal computations to uncover causal mechanisms rather than relying solely on post-hoc approximations. This approach treats neural networks as interpretable circuits, enabling precise interventions and debugging. A foundational effort was the 2022 Transformer Circuits project, which identified modular components like induction heads in attention layers, responsible for in-context learning patterns. Key advancements included the study of grokking, a phenomenon where overparameterized models abruptly transition from to after prolonged on small datasets. Observed in modular addition tasks, grokking revealed discrete phases in optimization, informing interpretability by highlighting how circuits form gradually before sudden performance leaps. This integration extended to sparse autoencoders (SAEs), applied from 2023 onward to decompose activations into human-interpretable features, such as monosemantic concepts in LLMs, mitigating superposition where neurons encode multiple abstract features. Anthropic's 2023 dictionary learning techniques scaled SAEs to billion-parameter models, extracting thousands of interpretable directions aligned with topics like safety or deception. Further developments emphasized hybrid methods combining local explanations with global circuit analysis. For instance, automated interpretability pipelines in 2024 used causal tracing to verify feature contributions across layers, enhancing fidelity in transformer explanations. These techniques addressed deep learning's opacity by enabling scalable interventions, such as editing specific circuits to alter model behavior without retraining. Despite progress, challenges persist in scaling to frontier models, where computational costs for circuit discovery grow superlinearly, prompting ongoing research into efficient approximation methods. Regulatory pressures, including the EU AI Act's requirements for high-risk systems effective from 2024, accelerated practical integrations of these tools in deployed deep learning applications.

Core Techniques

Inherently Interpretable Models

Inherently interpretable models, also termed intrinsically interpretable or white-box models, are algorithms designed such that their internal structure and prediction mechanisms are directly understandable by humans, obviating the need for post-hoc explanation tools applied to opaque systems. These models achieve transparency through properties like simulatability, where users can mentally replicate decisions in limited time, and decomposability, enabling intuitive grasp of inputs, parameters, and outputs. Unlike black-box models such as deep neural networks, which require surrogate explanations, inherently interpretable models embed comprehensibility in their architecture from the outset. Classic examples include linear and , where feature coefficients quantify the magnitude and direction of each variable's influence on outcomes, allowing direct assessment of importance and assumptions under . Decision trees, particularly shallow or optimal variants like Optimal Classification Trees (OCTs), represent decisions as hierarchical if-then rules tracing paths from root to nodes, with splits based on feature thresholds that users can inspect for logical consistency. Naive Bayes classifiers offer probabilistic interpretations via assumptions, decomposing predictions into feature likelihoods. These models suit domains demanding , as predictions can be audited without computational intermediaries. More advanced variants extend interpretability to nonlinear data while preserving transparency. Generalized additive models (GAMs) decompose predictions into additive sums of univariate nonlinear functions per feature, visualized as shape plots to reveal interactions without full additivity violations. Supersparse linear models (SLIMs) enforce coefficients and sparsity for concise, rule-like expressions, as in risk scoring where few terms dominate. Falling rule lists (FRLs) generate monotonic sequences of if-then rules, prioritizing higher- conditions first for ordinal outcomes like disease severity. Such extensions balance expressiveness with human oversight, though they impose constraints like sparsity or monotonicity to maintain comprehensibility. Despite advantages in trust-building and —evident in healthcare applications where OCTs achieve area under the curve (AUC) values of 0.638–0.675 for cancer prognostication, rivaling complex models like (AUC 0.654–0.690)—these models often trade predictive power for simplicity, underperforming on intricate, high-dimensional datasets with nonlinearities or interactions. Evaluations highlight via functional-grounded metrics (e.g., matching predictions) but reveal challenges in universal definitions and human-grounded assessments, where perceived utility varies by expertise. In practice, selection favors them when accuracy thresholds permit, prioritizing causal insight over marginal gains in opaque alternatives.

Post-Hoc Local Explanation Methods

Post-hoc local explanation methods generate instance-level interpretations for black-box models after training, focusing on approximating the model's near a specific input without altering the model's or parameters. These approaches prioritize locality by emphasizing explanations valid in the neighborhood of the instance, enabling users to understand why a particular output was produced for that case, which is particularly useful for high-stakes domains requiring per- . Unlike global methods, they trade broader model insights for detailed, context-specific rationales, often using surrogate approximations that balance interpretability and to the original . A foundational technique is Local Interpretable Model-agnostic Explanations (LIME), introduced by Ribeiro, Singh, and Guestrin in 2016. LIME operates by perturbing the input instance to create a dataset of synthetic samples, querying the black-box model for predictions on these perturbations, and then fitting a simple interpretable surrogate model—typically —weighted by proximity to the original instance to ensure local fidelity. The resulting feature weights indicate contributions to the prediction, visualized as bar charts or heatmaps for tabular, text, or image data. This model-agnostic method applies to classifiers like random forests or neural networks, with empirical evaluations on datasets such as those from the UCI repository showing it approximates predictions within 5-10% error locally in many cases. SHapley Additive exPlanations (SHAP), proposed by Lundberg and Lee in 2017, extends cooperative game theory's Shapley values to attribute prediction outcomes additively to input features. For a given instance, SHAP computes exact or approximate marginal contributions of each feature by considering all possible coalitions of features, marginalizing over the model's behavior, and ensures consistency properties like efficiency (attributions sum to the prediction) and local accuracy (explaining deviations from expected output). Kernel SHAP approximates these values efficiently via weighted on sampled coalitions, while TreeSHAP leverages structures for exact computation in polynomial time. Evaluations on benchmarks like subsets demonstrate SHAP's attributions correlate strongly with human-annotated importance, outperforming LIME in consistency across perturbations by up to 20% in some studies. Other variants include permutation-based methods like feature permutation importance localized via repeated sampling around the instance, which measures prediction degradation upon feature shuffling while preserving correlations, though they risk confounding effects in high-dimensional spaces. Counterfactual local explanations generate minimal input changes yielding alternative predictions, optimized via gradient descent or genetic algorithms to highlight decision boundaries, with studies on loan approval models showing they reveal actionable insights missed by additive methods. These techniques share advantages in flexibility across model types but face challenges: LIME's explanations can vary unstably with sampling seeds (up to 15% variance in feature rankings per 2019 robustness analyses), SHAP's exact computation scales exponentially with features (mitigated by approximations introducing bias), and both may overemphasize spurious correlations if perturbations inadequately capture the model's inductive biases. Validation often relies on metrics like local accuracy (prediction match) and stability (consistency under noise), with comparative benchmarks indicating SHAP generally achieves higher faithfulness at greater computational expense—e.g., 10-100x slower than LIME for deep networks.

Post-Hoc Global Explanation Methods

Post-hoc global explanation methods apply interpretive techniques to already-trained models, focusing on their overall predictive patterns across an entire dataset rather than individual instances. These model-agnostic approaches generate approximations or visualizations that reveal aggregate feature influences and decision boundaries without modifying the original black-box predictor, enabling stakeholders to understand systemic behaviors such as dominant feature interactions or patterns. Unlike local methods, which probe specific predictions, global methods prioritize comprehensiveness, though they risk oversimplification if the black-box exhibits high non-linearity or heterogeneity. Global surrogate models represent a core technique, wherein an interpretable proxy—such as , s, or rule-based systems—is trained to replicate the black-box model's outputs using the same input features and target predictions. is quantified through metrics like or accuracy on held-out data, with higher surrogate performance indicating reliable insights into the black-box's logic; for instance, a surrogate might yield hierarchical feature rules mirroring the complex model's priorities. This method, applicable to any black-box, traces origins to early efforts in approximating neural networks but gained prominence in XAI for its balance of transparency and scalability, as evidenced in benchmarks where tree surrogates achieved over 90% on tabular datasets. Limitations include potential loss of subtle interactions if the surrogate class is overly simplistic, prompting hybrid selections based on . Permutation feature importance provides another post-hoc global metric, evaluating each feature's aggregate contribution by randomly shuffling its values in the validation set and measuring the resulting degradation in model performance, such as increased or AUC drop. Features causing the largest error spikes rank highest in importance, offering a baseline-agnostic view independent of model internals; Breiman originally applied this in random forests in , but it extends post-hoc via implementations in libraries like , where it has been validated on datasets like UCI benchmarks to identify spurious correlations missed by embedded methods. Critics note sensitivity to dataset noise and , which can inflate or deflate scores, necessitating multiple permutations—typically 10–100—for stability. Partial dependence plots (PDPs) visualize the marginal effect of one or two features on predictions by averaging the model's output over all other features' distributions, effectively isolating average trends while assuming feature independence. Introduced by in 2001 for tree ensembles, PDPs extend post-hoc to any model and reveal non-linear relationships, such as monotonic increases or thresholds; for example, in models, a PDP might show loan approval probability plateauing beyond income levels of $100,000. Individual conditional expectation (ICE) plots extend this by plotting per-instance curves, allowing detection of heterogeneous effects when aggregated into fan-like visuals. Both techniques, implemented in tools like since 2010, falter with strongly correlated features, leading to extrapolated artifacts, as demonstrated in simulations where PDPs misrepresented interactions by up to 20% in high-dimensional data. Accumulated local effects (ALE) plots mitigate this by conditioning on local neighborhoods, preserving correlation handling while maintaining global scope. Prototypes and counterfactuals can aggregate globally by clustering data into representative exemplars or generating high-level rules from perturbation analyses, though these often blend local insights; for instance, SHAP values, derived from game-theoretic axioms, can summarize into global importance rankings via mean absolute values across instances, correlating strongly with permutation scores in empirical tests on subsets (r > 0.8). Validation remains challenging, with studies showing surrogate fidelity dropping below 70% for deep neural networks on image tasks due to distributional shifts, underscoring the need for domain-specific benchmarks.

Emerging Hybrid and Causal Approaches

Hybrid approaches in explainable artificial intelligence (XAI) integrate elements of inherently interpretable models, such as decision trees or linear regressions, with high-performance black-box models like deep neural networks to achieve a balance between predictive accuracy and human-understandable explanations. This strategy addresses the limitations of purely interpretable models, which often sacrifice performance on complex tasks, by leveraging the strengths of opaque models while approximating their decisions through transparent proxies or techniques. For instance, a 2020 study proposed a hybrid framework that distills explanations from predictions into rule-based forms, enabling post-hoc interpretability without retraining the core model. Recent advancements, documented in 2024 reviews, classify these hybrids by interpretability focus, such as local versus global explanations, and highlight applications in domains requiring , where black-box accuracy is augmented by symbolic reasoning layers. Causal approaches emphasize modeling cause-and-effect relationships to provide explanations grounded in interventions and counterfactuals, moving beyond correlational feature attributions common in traditional XAI methods. Drawing from Judea Pearl's causal hierarchy—which distinguishes association, intervention, and counterfactual reasoning—these methods construct directed acyclic graphs (DAGs) or structural causal models (SCMs) to infer how changes in inputs would affect outcomes, offering verifiable insights into model behavior under hypothetical scenarios. A 2023 of over 100 studies found that enhances XAI by enabling robust explanations resilient to variables, with applications in detection and policy simulation. For example, counterfactual explanations generate minimal input perturbations that flip predictions, quantifying causal contributions more reliably than saliency maps, as validated in controlled experiments on tabular and . Emerging hybrid causal frameworks combine these paradigms to yield "truly explainable" systems that maintain to causal structures while scaling to large . In 2025, the Holistic-XAI (H-XAI) framework integrated causal rating mechanisms—assessing intervention effects via do-calculus—with feature attribution tools like SHAP, demonstrating improved explanation stability in dynamic environments such as healthcare diagnostics. Neuro-symbolic hybrids further blend neural networks for with symbolic causal engines for logical inference, as explored in 2025 prototypes for agent-based , where causal graphs constrain neural outputs to ensure interventions align with real-world . These developments, often tested on benchmarks like causal discovery tasks from the IHDP , report up to 20% gains in counterfactual accuracy over non-causal baselines, underscoring their potential for reliable AI deployment in high-stakes settings. However, challenges persist in automating causal discovery from observational data, where assumptions like Markov faithfulness must be empirically validated to avoid spurious inferences.

Evaluation and Validation

Metrics for Explanation Fidelity and Comprehensibility

Explanation fidelity metrics evaluate the alignment between an and the black-box model's actual predictions, often through perturbation tests that measure prediction changes when features are altered based on the explanation's attributions. , a core fidelity metric, quantifies this by assessing how well removing or masking features ranked by importance affects model output; for instance, deletion-based faithfulness computes the between attribution scores and the drop in prediction confidence as high-importance features are sequentially removed. Insertion AUC, conversely, measures fidelity by progressively adding features from least to most important per the explanation and tracking rising model accuracy, with higher AUC values indicating stronger alignment. , another perturbation metric, calculates the Pearson between feature importance scores from the explanation and corresponding changes in model predictions under masking, achieving near-perfect scores on linear models but varying across complex architectures. Plausibility, distinct from faithfulness, refers to how convincing explanations appear to humans, particularly in the context of self-explanations from large language models, where recent research highlights the gap between plausible but unfaithful outputs that seem logical yet do not align with the model's internal processes. These metrics reveal limitations, such as sensitivity to perturbation strategies; for example, ground-truth assumes access to true model internals, which is infeasible for opaque models, while predictive relies on proxy behaviors like output shifts. Studies using decision trees as transparent proxies have verified that metrics like estimate and correlation yield consistent rankings of explanation methods, though they underperform on non-monotonic relationships without causal adjustments. Comprehensive reviews classify under representational metrics, emphasizing its distinction from stability, where explanations should remain consistent across similar inputs. Comprehensibility metrics assess human interpretability, prioritizing subjective and objective proxies for how easily users grasp explanations. User satisfaction and mental model accuracy, evaluated via surveys or tasks where participants predict model outputs from explanations, gauge perceived clarity; for example, comprehension tests in controlled studies score users' ability to infer feature influences correctly. Objective measures include explanation sparsity (e.g., number of highlighted features) or syntactic simplicity (e.g., rule length), which correlate with faster human processing in domains like tabular data. Datasets from user studies on XAI methods, such as LIME or SHAP visualizations, quantify comprehensibility through Likert-scale ratings of understandability and transparency, revealing domain-specific variances like higher scores for visual over textual formats in image tasks. Trade-offs persist, as high-fidelity explanations (e.g., dense attribution maps) often reduce comprehensibility due to cognitive overload, necessitating hybrid evaluations combining automated with human-centered proxies. lags, with taxonomies proposing multi-aspect frameworks including effectiveness and trust, but empirical validation shows inter-metric correlations below 0.7, underscoring the need for context-aware benchmarks.

Human-Centered Assessment Challenges

Human-centered assessments in explainable artificial intelligence (XAI) seek to measure how explanations influence users' understanding, trust, and processes, often through empirical user studies that gauge subjective outcomes like perceived utility and mental models formed. These evaluations prioritize end-user perspectives over purely technical metrics, yet they encounter persistent difficulties in establishing reliable, objective benchmarks due to the interplay of cognition and contextual factors. A core challenge stems from the subjectivity inherent in human judgments, where explanations' perceived fidelity and helpfulness vary widely based on users' prior , cognitive biases, and social influences, complicating consensus on what constitutes an effective . Without a universal for explanations—unlike verifiable model predictions—assessors struggle to differentiate genuine from superficial or illusory comprehension, often relying on self-reported prone to overconfidence or anchoring effects. Standardization remains elusive, as studies employ ad-hoc protocols and metrics (e.g., Likert-scale surveys for trust or task performance proxies for understanding), yielding incomparable results across domains and precluding meta-analyses or broad validation. This fragmentation is exacerbated by sparse incorporation of principles, such as theory or , which could ground evaluations but are rarely operationalized systematically. Participant diversity poses further hurdles, with many studies drawing from convenience samples like students or AI experts, underrepresenting end-users such as clinicians, policymakers, or non-technical stakeholders whose needs differ in expertise and tolerance. Evaluations thus often overlook variations in user backgrounds, leading to designs that fail in real-world deployment where heterogeneous groups interact with AI. The logistical demands of user studies—requiring ethical oversight, controlled experiments, and sufficient power for —limit their scale and frequency, resulting in sparse evidence bases that hinder and long-term tracking of efficacy. Consequently, human-centered assessments risk prioritizing narrow, context-bound findings over robust, generalizable insights, potentially misguiding XAI development toward superficial transparency rather than causal or mechanistic understanding.

Benchmarks and Standardization Efforts

Benchmarks in explainable artificial intelligence (XAI) aim to provide standardized frameworks for evaluating the , robustness, and comprehensibility of methods, addressing the absence of universal metrics in the field. These benchmarks typically involve synthetic or real-world datasets paired with ground-truth explanations or controlled model behaviors to test post-hoc methods like feature attribution. For instance, the M4 benchmark, introduced in 2023, unifies faithfulness evaluation across modalities such as images, text, and graphs using consistent metrics like sufficiency and comprehensiveness scores. Similarly, XAI-Units, released in 2025, employs unit-test-like evaluations on datasets with known causal mechanisms to assess feature attribution methods against diverse failure modes, revealing inconsistencies in popular techniques like SHAP and LIME. Several open-source toolkits facilitate large-scale benchmarking. BenchXAI, a comprehensive package from 2025, evaluates 15 post-hoc XAI methods on criteria including robustness to perturbations and suitability for tabular data, highlighting limitations such as sensitivity to hyperparameter choices. The BEExAI framework, proposed in 2024, enables comparisons via metrics like explanation stability and alignment with human judgments on classification tasks. Visual XAI benchmarks often draw from curated datasets, such as the eight-domain collection covering object and , which tests explanation faithfulness against perturbation-based proxies. These efforts underscore a shift toward modular, extensible platforms, though surveys note persistent gaps in toolkit interoperability and coverage of global surrogates. Standardization efforts focus on establishing principles and protocols to mitigate evaluation inconsistencies, driven by regulatory pressures for trustworthy AI. The U.S. National Institute of Standards and Technology (NIST) outlined four principles for XAI systems in 2021—explanation, meaning, validity, and soundness—to guide development and assessment, emphasizing empirical validation over subjective interpretations. In , initiatives like CEN workshop agreements promote metadata standards and procedural guidelines for XAI-FAIR practices, aiming to harmonize explainability across AI/ML pipelines. Despite these, full standardization remains elusive due to domain-specific challenges, such as varying notions of "" in high-stakes applications, prompting calls for unified metrics in peer-reviewed benchmarks. Ongoing work, including open benchmarks like OpenXAI, seeks to enforce rigorous, reproducible evaluations to support regulatory compliance.

Key Applications

Healthcare and Biomedical Decision Support

Explainable artificial intelligence (XAI) plays a critical role in healthcare by elucidating the reasoning behind AI models used in clinical decision support systems (CDSS), where opaque predictions can undermine clinician trust and . In biomedical applications, XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) attribute feature importance to model outputs, enabling verification of diagnostic or prognostic decisions against medical knowledge. For instance, in analysis for cancer detection, post-hoc methods like Grad-CAM generate heatmaps highlighting regions influencing classifications, allowing radiologists to cross-check AI suggestions with visual evidence. In treatment planning and , XAI supports outcome prediction by revealing causal factors in patient data, such as genetic markers or comorbidities driving therapy recommendations. A 2024 study on optimizing clinical alerts used XAI to refine alert criteria in electronic health records, identifying key variables like and lab results that reduced false positives by prioritizing interpretable features over black-box performance alone. Similarly, in and biomarker identification, XAI has been applied to data for , where models explained predictions by linking patterns to disease progression, aiding validation of potential therapeutic targets. Empirical evidence indicates XAI enhances adoption: a review of 5 studies found that clear, relevant explanations increased clinicians' trust in AI over unexplainable models, particularly in high-stakes scenarios like prediction or surgical . In (TBI) forecasting, a comparing methods deemed SHAP most stable and faithful to model behavior, while rule-based anchors provided the highest comprehensibility for tabular clinical data. However, challenges persist, including ensuring explanations align with domain expertise—e.g., avoiding misleading attributions in heterogeneous biomedical datasets—and validating fidelity through feedback loops. Biomedical decision support also leverages XAI for epidemic response, as seen in prognosis models where explanations traced predictions to symptoms and biomarkers, improving accuracy during the 2020-2022 . Regulatory bodies like the FDA emphasize explainability in approved AI devices, mandating transparency for high-risk uses such as cardiovascular risk stratification, though integration requires balancing model accuracy with interpretive depth. Overall, XAI mitigates risks of over-reliance on AI by empowering evidence-based overrides, though ongoing stresses human-AI to address biases in training data from diverse populations.

Financial Risk Assessment and Compliance

In financial risk assessment, explainable artificial intelligence (XAI) techniques enable the interpretation of opaque models used for predicting credit defaults, market volatility, and operational risks, revealing feature contributions such as borrower debt-to-income ratios or that drive predictions. For instance, SHAP (SHapley Additive exPlanations) values can quantify how specific variables, like transaction velocity in fraud models, influence risk scores, allowing analysts to trace causal pathways from inputs to outputs without relying on black-box approximations. This interpretability supports empirical validation against historical data, where studies have shown XAI-enhanced models reducing unexplained variances in forecasts by up to 20% compared to non-interpretable counterparts. Regulatory compliance in finance increasingly demands such transparency, as black-box AI decisions risk violating mandates for auditability and non-discrimination; under the EU AI Act, effective August 1, 2024, high-risk systems in creditworthiness evaluation and must provide explanations of decision logic to users, with phased enforcement starting February 2025 for general obligations and August 2027 for high-risk compliance. In anti-money laundering (AML) applications, XAI elucidates flagged transactions by highlighting indicators like transfers from high-risk jurisdictions or anomalous patterns, facilitating demonstrable adherence to standards such as the U.S. Bank Act or FATF recommendations, where unexplained alerts have historically led to regulatory fines exceeding $10 billion annually across global banks. XAI also mitigates compliance risks in and , where global surrogates or counterfactual s justify portfolio risk allocations under frameworks like , which require institutions to articulate model assumptions for supervisory review. In high-frequency trading, XAI techniques such as SHAP, LIME, and attention mechanisms explain model decisions by attributing importance to influential features, such as specific tick patterns or liquidity indicators, providing partial insights into prediction dynamics. Empirical deployments, such as those in European banks post-2022, have integrated local explanation methods like LIME to comply with GDPR's , reducing dispute rates in automated lending decisions by providing borrower-specific rationales tied to verifiable data points. However, while XAI enhances accountability, its effectiveness hinges on robust validation against adversarial inputs, as unaddressed biases in explanation proxies could undermine regulatory trust, with peer-reviewed analyses noting persistent gaps in global model fidelity for high-dimensional financial datasets.

Public Policy and Social Choice Mechanisms

In , explainable artificial intelligence (XAI) supports processes by providing interpretable models for simulation, , and impact forecasting, enabling policymakers to audit causal pathways and mitigate unintended biases. For instance, AI-driven tools for predicting outcomes, such as economic stimulus effects or environmental impacts, incorporate techniques like LIME or SHAP to decompose predictions into feature contributions, fostering in governmental applications. Empirical studies demonstrate that supplying explanations for AI-generated recommendations enhances stakeholder trust and acceptance, with one experiment showing improved attitudes toward automated government decisions when rationales were provided, though the effect varied by explanation type such as feature-based versus counterfactual. However, integrating XAI into public policy reveals trade-offs, as demands for interpretability can constrain model complexity and accuracy, potentially undermining effective in high-stakes scenarios like welfare distribution or crisis response. Brookings analysis highlights that while explainability counters risks of opaque AI reinforcing biases, it may conflict with objectives requiring nuanced, non-linear predictions, such as in adaptive fiscal planning where black-box models outperform interpretable ones in forecast precision. Moral arguments emphasize XAI's role in upholding democratic legitimacy, arguing that transparent algorithms in tools prevent arbitrary power exercises and align with principles of . In social choice mechanisms, XAI addresses challenges in aggregating heterogeneous preferences for collective decisions, such as voting systems or division, by rendering algorithmic aggregators auditable to detect manipulation or inequity. Randomized voting rules enhanced with explainability, for example, use post-hoc techniques to justify probabilistic outcomes, ensuring voters comprehend how individual rankings influence final tallies and reducing perceptions of in multi-winner elections. Frameworks drawing from learning theory propose representative social choice models where AI aligns with diverse voter preferences through interpretable bounds, applicable to policy referenda or , though empirical validation remains limited to simulated environments as of 2024. These approaches prioritize causal transparency over mere correlational outputs, aiding verification of in mechanisms like adaptations. Despite potential, issues persist, as explaining intricate preference profiles in large electorates demands computationally efficient XAI methods without sacrificing fidelity to ground-truth utilities.

Wind Power Scheduling and Electricity Market Trading

In wind power scheduling and electricity market trading, explainable artificial intelligence (XAI) mitigates the opacity of AI prediction models for renewable energy sources, enabling broader deployment by clarifying decision logic amid uncertainties in generation and prices. Interpretability allows regulators to evaluate prediction rationales for risk assessment, supporting transparent policy development and market reliability. Engineers utilize XAI to comprehend model decisions, optimizing scheduling and bidding; for example, symbolic regression evolves interpretable policies that minimize imbalance costs and maximize revenue, incorporating expert knowledge for robustness in extreme conditions. By addressing black-box barriers, XAI fosters stakeholder trust, as evidenced in operational datasets where interpretable models enhance trading efficacy and acceptability without compromising performance.

Regulatory and Policy Dimensions

Existing Frameworks and Mandates

The European Union's Artificial Intelligence Act (Regulation (EU) 2024/1689), published on July 12, 2024, and entering into force on August 1, 2024, establishes the world's first comprehensive binding regulatory framework for AI, with phased applicability starting February 2, 2025, and full enforcement by August 2, 2027. It adopts a risk-based approach, mandating transparency and explainability obligations primarily for "high-risk" AI systems—those deployed in areas like biometric identification, critical infrastructure, education, employment, and law enforcement—defined as systems presenting significant potential harm to health, safety, or fundamental rights. Providers of high-risk systems must conduct fundamental rights impact assessments, maintain detailed technical documentation on data sources, model training, and decision logic, and ensure systems are transparent enough for deployers and affected persons to understand outputs, including human-readable explanations of decisions where feasible; failure to comply can result in fines up to €35 million or 7% of global annual turnover. The Act also requires logging of operations for traceability and post-market monitoring, though it exempts general-purpose AI models unless adapted for high-risk use, reflecting a pragmatic acknowledgment of technical limits in achieving full interpretability for opaque "black-box" systems. Regulatory discussions of explainability often conflate it with accountability requirements. While explanations aid user comprehension of specific outputs, they do not inherently establish system responsibility, version provenance, or post-hoc auditability. Accountability in governance relies instead on infrastructural mechanisms, such as persistent identifiers, versioned data corpora, operational logging, and machine-readable provenance, enabling traceability of AI-mediated decisions over time. Complementing the AI Act, the General Data Protection Regulation (GDPR), effective since May 25, 2018, imposes constraints on under Article 22, prohibiting decisions based solely on automated processing—including profiling—that produce legal effects or similarly significant impacts on individuals, unless explicitly authorized or necessary for performance, with safeguards like the right to human intervention, expression of views, and "an of the decision reached." Recital 71 clarifies that such explanations should detail the logic involved, though courts and scholars its scope, interpreting it as requiring meaningful, non-generic rationales rather than full algorithmic disclosure to balance data protection with proprietary interests; enforcement has yielded fines, such as the €9.5 million penalty against in 2022 for opaque facial recognition practices lacking adequate explanations. This framework influences XAI by incentivizing interpretable models in contexts but stops short of a universal "," prioritizing contestability over exhaustive transparency. In the United States, the National Institute of Standards and Technology's AI Risk Management Framework (AI RMF 1.0), released on January 26, 2023, provides a voluntary, non-binding guideline for managing AI risks across the lifecycle, emphasizing "explainability and interpretability" as core to trustworthiness characteristics like transparency and . It outlines practices for mapping risks (e.g., identifying opacity in decision processes), measuring outcomes (e.g., via fidelity metrics for post-hoc explanations), and managing mitigations (e.g., hybrid models combining accuracy with comprehensibility), without prescriptive mandates but encouraging alignment with sector-specific regulations like those from the on deceptive AI practices. The framework's flexibility accommodates diverse AI deployments but relies on self-assessment, with updates planned iteratively based on stakeholder input. Internationally, the for Standardization's ISO/IEC 42001:2023, published in December 2023, sets requirements for AI management systems, integrating explainability into governance controls for ethical deployment, , and continuous monitoring, applicable to organizations worldwide seeking . Similarly, ISO/IEC 22989:2022 defines key terms like "explainability" as the capacity to express factors influencing outputs, while ISO/IEC TR 24028:2020 (updated contexts) guides management of and fairness, promoting auditable transparency without legal enforcement. These standards facilitate compliance with binding regimes like the EU AI Act but remain advisory, highlighting a global patchwork where mandates cluster in high-stakes domains amid ongoing debates on enforceability for inherently complex neural networks.

Debates on Mandatory Explainability

Advocates for mandatory explainability in high-risk AI systems argue that it ensures accountability and trust, particularly in domains like healthcare and where decisions impact and safety. For instance, the European Union's AI Act, effective from August 1, 2024, mandates transparency obligations for high-risk systems, including documentation of decision-making processes to allow human oversight and contestability of outputs, aiming to mitigate biases and errors through verifiable explanations. Proponents, including regulatory bodies, contend that such requirements align with broader legal principles like the GDPR's emphasis on meaningful information about automated decisions, enabling users to challenge outcomes and fostering ethical deployment. This perspective holds that without enforced explainability, opaque models risk unchecked errors, as evidenced by cases where black-box AI in lending or diagnostics has perpetuated discrimination without recourse. Critics, however, warn that mandating explainability imposes undue burdens, often trading off predictive accuracy for superficial transparency, as complex neural networks derive efficacy from non-linear interactions not easily distilled into human-readable forms. Studies show that interpretable models like decision trees frequently underperform counterparts by 5-20% in accuracy on high-dimensional tasks, suggesting mandates could stifle in critical applications. Moreover, post-hoc explanation techniques, commonly proposed for compliance, can produce inconsistent or misleading rationales that create a "false sense of ," eroding rather than enhancing by masking underlying uncertainties. In the EU AI Act context, opponents highlight enforcement gaps and loopholes that prioritize general transparency over rigorous explainability, potentially slowing European AI competitiveness without proportional risk reduction. The debate extends to feasibility, with empirical evidence indicating that true causal interpretability remains elusive for scaled models trained on vast datasets, as approximations fail to capture emergent behaviors. Alternatives like rigorous validation through outcome testing and auditing are proposed over blanket mandates, arguing that over-reliance on explanations could divert resources from robust performance metrics. This tension reflects broader policy challenges, where mandatory explainability risks regulatory capture by interpretable-but-suboptimal methods, while voluntary approaches in less-regulated jurisdictions like the have accelerated advancements without evident safety trade-offs.

International Variations and Enforcement Issues

The European Union's , adopted on May 21, 2024, and entering into force progressively from August 2024, imposes mandatory transparency and explainability requirements on high-risk AI systems, such as those used in biometric identification or , requiring providers to ensure systems are designed for human oversight and to provide deployers with sufficient information to interpret outputs. In contrast, the lacks a comprehensive federal AI law as of October 2025, relying instead on voluntary guidelines like the National Institute of Standards and Technology's (NIST) four principles of explainable AI—explanation, meaning, actionability, and justification—which emphasize measurement and policy support without enforceable mandates, alongside sector-specific agency policies such as the Office of Management and Budget's April 2025 memo promoting inherently explainable models in federal use. China's Interim Measures for the Management of Services, effective August 15, 2023, and subsequent frameworks like the September 2024 Governance Framework, mandate transparency and explainability principles for AI developers and providers, requiring clear disclosure of sources and algorithmic logic to ensure accountability, though enforcement prioritizes state oversight and over user-centric interpretability. Other jurisdictions exhibit further divergence; for instance, the United Kingdom's pro-innovation approach under its 2023 AI White Paper avoids binding explainability rules, favoring sector-specific regulators, while emerging frameworks in countries like and emphasize voluntary transparency aligned with principles but lack uniform enforcement mechanisms. Enforcement challenges arise from these inconsistencies, including difficulties in verifying compliance for opaque models, as regulators struggle to assess whether explanations accurately reflect causal decision processes without standardized metrics, leading to potential over-reliance on self-reported disclosures by providers. Cross-border operations exacerbate issues, with multinational firms facing regulatory risks—such as deploying less stringent U.S.-style voluntary guidelines to evade EU fines up to 6% of global turnover—and jurisdictional conflicts that hinder unified oversight, particularly for cloud-based AI systems spanning regions. Resource constraints in lower-capacity enforcers, combined with trade-offs between explainability and model performance, further complicate audits, as demonstrated by early EU cases where providers contested interpretability requirements due to technical infeasibility in high-dimensional systems. Absent global efforts, such as those proposed in international forums, these variations foster fragmented and uneven protection against AI misuse.

Limitations and Trade-Offs

Performance-Explainability Conflicts

In , models achieving state-of-the-art predictive performance, such as deep neural networks and ensemble methods like random forests, frequently exhibit reduced interpretability compared to simpler alternatives like or single decision trees, as complexity enables capturing nonlinear interactions but obscures causal pathways. This tension manifests statistically when interpretability constraints—restricting models to transparent hypothesis classes—increase excess risk, leading to accuracy losses on high-dimensional or nonlinear data. Empirical evidence underscores domain-specific variations in the trade-off's severity. For instance, in a 2022 user study across (Portuguese student performance dataset, 1,044 samples, 33 features) and (King County prices, 21,613 samples, 20 features) domains, black-box models outperformed interpretable ones in precision at 25% (0.85 vs. 0.78 for housing), yet participants rated black-boxes with post-hoc explanations (e.g., SHAP) as equally explainable, challenging assumptions of inherent opacity. Conversely, in tasks, black-box models consistently surpassed interpretable baselines in accuracy, with performance degrading as constraints enforced greater transparency. In high-stakes contexts like healthcare or , this conflict favors inherently interpretable models over black boxes with explanations, as post-hoc methods risk misleading interpretations without guaranteeing fidelity to the underlying decision process. Cynthia Rudin argues that optimized interpretable models—such as sparse rule lists or generalized additive models—can approach black-box performance in targeted applications, avoiding explanation unreliability while enabling direct auditing and improvement. Ongoing thus explores hybrid approaches, like distilling black-box knowledge into interpretable surrogates, to mitigate losses without fully sacrificing accuracy.

Scalability Issues in High-Dimensional Data

High-dimensional data, such as genomic sequences with thousands of features or images represented by millions of pixels, pose significant scalability challenges for explainable AI (XAI) methods due to the curse of dimensionality, where the volume of the feature space grows exponentially with the number of dimensions, complicating both computation and interpretability. Perturbation-based techniques like LIME and SHAP, which generate explanations by approximating local model behavior through repeated sampling and model evaluations, exhibit computational complexity that scales poorly; LIME's complexity grows quadratically with the number of features, while SHAP's approximations, such as KernelSHAP, can demand exponential resources relative to feature count, often rendering exact explanations infeasible for datasets exceeding hundreds of dimensions. For instance, computing exact Shapley values in high-dimensional spaces requires evaluating coalitions of features, which becomes prohibitive without approximations that may introduce noise or bias, particularly in unstructured data like medical imaging or tabular datasets from finance. These issues manifest in reduced explanation fidelity, as high-dimensional sparsity leads to unreliable feature attributions; in deep neural networks trained on such data, methods like saliency maps or integrated gradients provide pixel-level insights but struggle to aggregate meaningful global patterns without , which risks omitting causal interactions. Empirical studies on datasets like those in bioinformatics highlight that post-hoc XAI tools demand excessive runtime—often hours or days per instance—for models with over 1,000 features, limiting real-time deployment in applications such as or autonomous systems. Moreover, the combinatorial explosion in perturbation sampling exacerbates hardware constraints, with GPU acceleration offering partial mitigation but failing to address the fundamental . Efforts to mitigate scalability include hybrid approaches combining XAI with dimensionality reduction techniques like PCA or autoencoders prior to explanation generation, though these trade off completeness for efficiency and may propagate reduction-induced artifacts into interpretations. Despite approximations enabling practical use in moderate high-dimensional settings (e.g., up to 10,000 features with sampling heuristics), full remains elusive for ultra-high dimensions, as evidenced by persistent computational bottlenecks in benchmarks involving convolutional neural networks on image data. This underscores a core in XAI: while intrinsic interpretable models avoid such costs, they often underperform black-box alternatives on high-dimensional tasks, prioritizing accuracy over explainability.

Vulnerability to Adversarial Manipulation

Explainable artificial intelligence (XAI) methods are vulnerable to adversarial manipulation, where attackers craft imperceptible perturbations to inputs that distort the explanations provided by the system while leaving the underlying model's predictions largely unchanged. This phenomenon, often termed "explanation attacks," exploits the sensitivity of post-hoc interpretability techniques, such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), which approximate model behavior locally and can be fooled by inputs optimized to mislead these approximations. For instance, in white-box scenarios where attackers access the model's internals, perturbations can invert feature importance rankings in SHAP values, attributing causality to irrelevant features. Empirical studies demonstrate high success rates for such attacks across common XAI frameworks. A 2024 evaluation of LIME, SHAP, and Integrated Gradients on image classification tasks using datasets like CIFAR-10 showed that black-box attacks achieved explanation distortion rates exceeding 80% under minimal perturbation norms (e.g., L-infinity norm of 0.01), without altering prediction accuracy beyond 5%. In cybersecurity contexts, adversarial examples have manipulated XAI outputs in intrusion detection systems, causing explainers to highlight benign features as malicious, potentially evading defenses. These vulnerabilities extend to inherently interpretable models, such as attention-based mechanisms in transformers, where gradient-based attacks can shift focus to non-causal tokens, as observed in natural language processing benchmarks with attack success rates up to 95% on GLUE datasets. The mechanisms underlying these susceptibilities stem from the non-robust optimization landscapes of XAI methods, which prioritize fidelity to the black-box model over adversarial invariance. Attackers typically formulate objectives to maximize divergence between original and perturbed explanations—measured via metrics like of attribution maps—subject to constraints on prediction stability and perturbation boundedness. For example, projected gradient descent has been adapted to generate such examples, revealing that XAI's reliance on surrogate models or sampling introduces exploitable instabilities not present in raw predictions. In high-stakes applications, these manipulations undermine user trust and decision ; a relying on an adversarially perturbed XAI for might misinterpret benign anomalies as pathological, leading to erroneous interventions. Surveys of over 50 studies indicate that while prediction-robust (e.g., adversarial with PGD) improves model resilience, it often degrades , with SHAP dropping by 20-30% on robustified models tested on subsets. This highlights a core : enhancing XAI robustness requires integrating defenses like or certified bounds, yet current methods remain computationally intensive and incomplete, with certified robustness verified only for small perturbations in low-dimensional settings.

Criticisms and Controversies

Doubts on True Interpretability for Complex Systems

Skeptics of explainable AI contend that achieving genuine interpretability in complex systems, such as deep neural networks with billions of parameters and layered non-linear transformations, is fundamentally constrained by the models' internal opacity, where decision pathways defy reduction to human-comprehensible causal mechanisms. Rudin argues that post-hoc techniques applied to black-box models produce unreliable approximations rather than faithful representations of internal logic, as they cannot reliably distinguish true drivers from spurious correlations without sacrificing model . This view posits that distributed representations in neural networks—where knowledge is encoded across vast interconnections rather than localized features—preclude mechanistic understanding akin to dissecting simpler algorithms. Empirical assessments reinforce these doubts, demonstrating that even advanced interpretability tools fail to yield verifiable insights into model behavior. A 2023 study by researchers tested human interpretability of AI agents using formal logical specifications in a simulated capture-the-flag , finding participants achieved only approximately 45% accuracy in validating plans across formats like raw formulas, , and decision trees, with experts exhibiting overconfidence and overlooking modes. Such results suggest that purported explanations often mask rather than reveal the opaque computations underlying predictions, particularly in high-stakes domains requiring causal fidelity. Further empirical critiques highlight vulnerabilities in interpretability methods to statistical artifacts. A 2025 paper draws an analogy from a 2009 neuroscience study, which detected false brain activity in a dead Atlantic salmon via fMRI due to uncorrected multiple comparisons, to argue that AI interpretability techniques like linear probes and sparse autoencoders can generate plausible but spurious explanations even in randomly initialized, untrained models. These artifacts arise from statistical noise rather than genuine signal, underscoring the necessity of rigorous controls such as permutation tests, null hypothesis testing, and causal interventions to validate explanations against null models. Deeper challenges arise from "structure opacity," where models accurately predict outcomes tied to incompletely understood external phenomena, such as causal relations beyond current empirical grasp, rendering full interpretability unattainable without parallel advances in . Rudin emphasizes that explanations for complex models risk misleading users by implying transparency that does not exist, potentially eroding trust more than opacity itself, as they conflate empirical correlations with verifiable mechanisms. These limitations imply that for sufficiently intricate systems, interpretability efforts may at best provide surrogates, not true causal realism, echoing broader scientific hurdles in probing emergent properties of complex systems.

Risks of Over-Reliance and Misplaced Trust

Over-reliance on explainable (XAI) systems manifests as users uncritically deferring to AI outputs, even when explanations are provided, due to —the propensity to favor automated cues over independent judgment or contradictory evidence. This bias persists or intensifies with XAI because explanations can confer an of comprehension, prompting users to overestimate model reliability without verifying underlying assumptions or error rates. Empirical investigations reveal that non-informative or flawed explanations still elevate acceptance of incorrect AI recommendations; for example, in a 2019 study on AI-assisted tasks, users exposed to explanations exhibited higher trust in outputs with 50% accuracy compared to opaque systems, resulting in elevated error commissions. Misplaced trust arises particularly among non-experts, who often interpret XAI features like feature importance visualizations as guarantees of correctness, leading to overconfidence in high-stakes applications. A 2025 study found that lay users' trust in XAI explanations exceeded calibrated levels, with participants rating competence higher after viewing interpretability aids, even when subsequent AI errors contradicted them, thus amplifying decision risks. In healthcare contexts, this dynamic exacerbates harms: clinicians in a 2021 experiment were seven times more likely to endorse erroneous AI psychiatric diagnoses when supported by s, deferring to the system despite clinical expertise suggesting otherwise. Similarly, detailed rationales in clinical decision support increased reliance on flawed models among novice users, as shown in 2015 trials where explanation presence boosted endorsement of wrong predictions without improving overall accuracy. These risks compound in complex environments, where partial explanations (e.g., local surrogates like LIME) may highlight spurious correlations, fostering undue deference and downstream errors such as financial misallocations or diagnostic oversights. The "explanation paradox" underscores this: while XAI aims to calibrate reliance, it frequently induces higher confidence in erroneous outputs than black-box models, as users anchor on interpretive narratives rather than probabilistic uncertainties. Mitigation attempts, including uncertainty-aware explanations, yield inconsistent reductions in bias, with over-reliance persisting due to cognitive heuristics like toward provided justifications. In policy-sensitive domains, such patterns necessitate safeguards like mandatory human override protocols, though empirical evidence indicates explanations alone fail to avert systemic trust miscalibration.

Ideological Critiques and Hype Cycles

The pursuit of explainable artificial intelligence (XAI) has been characterized by pronounced hype cycles, mirroring Gartner's framework where technologies experience inflated expectations followed by disillusionment. Initial enthusiasm surged in the mid-2010s amid revelations of opacity in deployed systems, such as the 2016 analysis of the , which highlighted predictive disparities without clear causal mechanisms. This triggered a peak of optimism around 2018, coinciding with regulatory developments like the EU's (GDPR) Article 22, which implied a "right to explanation" for automated decisions, positioning XAI as a for accountability and . However, by the early 2020s, empirical evaluations revealed limitations, including the prevalence of post-hoc approximations like LIME and SHAP that prioritize local fidelity over global causal insight, leading to a trough of disillusionment as real-world applications exposed fidelity-performance trade-offs. Gartner's 2025 Hype Cycle for continues to feature XAI as a maturing domain, with vendors like SUPERWISE recognized for observability tools aimed at , yet the cycle underscores persistent challenges in scaling explainability amid pressures for fair and secure AI deployment. Businesses report implementation hurdles, as XAI methods often fail to deliver verifiable legitimacy for high-stakes decisions, contributing to skepticism about overhyped claims of enhanced trust. This phase reflects causal realism: complex models derive efficacy from distributed representations intractable to human-scale explanations, rendering many XAI techniques more performative than substantive, as evidenced by studies showing explanation instability across perturbations. Ideological critiques of XAI emphasize its alignment with precautionary paradigms in policy and academia, where demands for transparency prioritize normative ideals of human oversight over empirical outcomes from opaque systems. Brookings analyses argue that explainability does not resolve underlying political ambiguities in goals—such as balancing and equity in or welfare algorithms—but instead amplifies exposure to societal biases embedded in training data, potentially exacerbating distrust rather than alleviating it. This push, often amplified by institutions exhibiting systemic left-leaning biases toward interventionist frameworks, risks subordinating causal performance metrics to subjective interpretability standards, as seen in critiques of XAI's inability to legitimize decisions amid " hacking" vulnerabilities that allow manipulative rationalizations. Proponents of unencumbered AI advancement counter that such mandates ideologically constrain scaling laws, where historical data shows performance gains from complexity outweigh sporadic interpretability gains, though these views receive less traction in mainstream discourse due to prevailing regulatory narratives.

References

  1. https://ital.corejournals.org/index.php/ital/article/download/14683/11243/31221
  2. https://arxiv.org/pdf/2409.00265
  3. https://www.sciencedirect.com/science/article/pii/S1566253523001148
  4. https://nvlpubs.nist.gov/nistpubs/ir/2021/nist.ir.8312.pdf
  5. https://iphome.hhi.de/samek/pdf/HolXXAI22b.pdf
  6. https://www.mdpi.com/1424-8220/25/13/4166
  7. https://www.edps.europa.eu/system/files/2023-11/23-11-16_techdispatch_xai_en.pdf
  8. https://ieeexplore.ieee.org/document/10549884/
  9. https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1424
  10. https://www.sciencedirect.com/science/article/pii/S0950705123000230
  11. https://www.ibm.com/think/topics/black-box-ai
  12. https://www.activestate.com/blog/white-box-vs-black-box-algorithms-in-machine-learning/
  13. https://medium.com/biased-algorithms/white-box-vs-black-box-algorithms-in-machine-learning-af460de154ef
  14. https://christophm.github.io/interpretable-ml-book/overview.html
  15. https://www.sciencedirect.com/science/article/pii/S1389041724000378
  16. https://medium.com/%40prabhashdissanayake/understanding-the-dichotomy-local-vs-global-explanations-in-xai-c801087aed7a
  17. https://www.sciencedirect.com/science/article/pii/S0169260723002158
  18. https://arxiv.org/html/2507.23535v1
  19. https://arxiv.org/abs/2105.07190
  20. https://arxiv.org/abs/2409.00265
  21. https://doi.org/10.1016/j.inffus.2019.12.012
  22. https://doi.org/10.1145/3351095.3372870
  23. https://www.sei.cmu.edu/blog/what-is-explainable-ai/
  24. https://fiddler.ai/blog/explainable-ai
  25. https://rpc.cfainstitute.org/research/reports/2025/explainable-ai-in-finance
  26. https://aisel.aisnet.org/wi2023/77/
  27. https://arxiv.org/html/2402.10086v2
  28. https://ieeexplore.ieee.org/document/10829286/
  29. https://www.mdpi.com/2076-3417/14/19/8884
  30. https://pmc.ncbi.nlm.nih.gov/articles/PMC9122117/
  31. https://www.sciencedirect.com/science/article/pii/S2949697724000055
  32. https://www.sciencedirect.com/science/article/pii/S0950584923000514
  33. https://pmc.ncbi.nlm.nih.gov/articles/PMC11024755/
  34. https://dl.acm.org/doi/10.1145/3708319.3733707
  35. https://www.tandfonline.com/doi/full/10.1080/08839514.2025.2463722
  36. https://arxiv.org/abs/2408.02379
  37. https://zilliz.com/ai-faq/how-does-explainable-ai-contribute-to-ai-safety
  38. https://www.darioamodei.com/post/the-urgency-of-interpretability
  39. https://alignmentsurvey.com/materials/assurance/interpretability/
  40. https://www.lesswrong.com/posts/PwnadG4BFjaER3MGf/interpretability-will-not-reliably-find-deceptive-ai
  41. https://arxiv.org/abs/2402.10086
  42. https://multicorewareinc.com/explainable-ai-the-building-block-for-trustworthy-ai-systems/
  43. https://doi.org/10.1002/widm.1391
  44. https://arxiv.org/abs/2010.09337
  45. https://doi.org/10.1007/978-1-4757-1863-3
  46. https://www.darpa.mil/research/programs/explainable-artificial-intelligence
  47. https://dl.acm.org/doi/10.1145/3301275.3308446
  48. https://ggnpreprints.authorea.com/users/446266/articles/545563-darpa-s-explainable-ai-xai-program-a-retrospective
  49. https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2850
  50. https://nsarchive.gwu.edu/sites/default/files/documents/5794867/National-Security-Archive-David-Gunning-DARPA.pdf
  51. https://bdtechtalks.com/2019/01/10/darpa-xai-explainable-artificial-intelligence/
  52. https://esvorm.com/wp-content/uploads/2023/02/XAI-Retrospective.pdf
  53. https://xaitk.github.io/about/
  54. https://www.transformer-circuits.pub/2022/mech-interp-essay
  55. https://arxiv.org/abs/2407.02646
  56. https://zitniklab.hms.harvard.edu/publications/papers/interpretableML-survey20.pdf
  57. https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493
  58. https://pmc.ncbi.nlm.nih.gov/articles/PMC10007880/
  59. https://pmc.ncbi.nlm.nih.gov/articles/PMC7824368/
  60. https://christophm.github.io/interpretable-ml-book/lime.html
  61. https://www.aies-conference.com/2020/wp-content/papers/174.pdf
  62. https://www.researchgate.net/publication/384184280_Problems_with_SHAP_and_LIME_in_interpretable_AI_for_education_A_comparative_study_of_post-hoc_explanations_and_neural-symbolic_rule_extraction
  63. https://ieeexplore.ieee.org/document/10762963
  64. https://christophm.github.io/interpretable-ml-book/global.html
  65. https://medium.com/data-science/explainable-ai-xai-methods-part-5-global-surrogate-models-9c228d27e13a
  66. https://link.springer.com/article/10.1007/s12559-023-10179-8
  67. https://christophm.github.io/interpretable-ml-book/pdp.html
  68. https://scikit-learn.org/stable/modules/partial_dependence.html
  69. https://link.springer.com/article/10.1007/s12559-024-10325-w
  70. https://www.sciencedirect.com/science/article/pii/S187705092030394X
  71. https://ceur-ws.org/Vol-3803/paper4.pdf
  72. https://easychair.org/publications/preprint/fxbg/open
  73. https://arxiv.org/abs/2309.09901
  74. https://link.springer.com/article/10.1007/s11023-023-09637-x
  75. https://arxiv.org/html/2508.05792v1
  76. https://medium.com/data-science-collective/from-black-box-to-glass-box-a-practical-guide-to-implementing-xai-with-shap-on-a-hybrid-ai-agent-701c86048d74
  77. https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.70015
  78. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1439702/full
  79. https://www.sciencedirect.com/science/article/pii/S0925231224010531
  80. https://pmc.ncbi.nlm.nih.gov/articles/PMC12252469/
  81. https://arxiv.org/abs/2401.10640
  82. https://arxiv.org/abs/2402.04614
  83. https://ieeexplore.ieee.org/document/10297629
  84. https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1096257/full
  85. https://www.sciencedirect.com/science/article/pii/S0950584925000217
  86. https://www.nature.com/articles/s41597-025-05167-6
  87. https://www.dfki.de/fileadmin/user_upload/import/14708_XAI_Evaluation_Metrics__Taxonomies__Concepts_and_Applications__INES_2023_-7.pdf
  88. https://arxiv.org/abs/2210.11584
  89. https://arxiv.org/abs/2403.12730
  90. https://arxiv.org/abs/2410.21183
  91. https://proceedings.neurips.cc/paper_files/paper/2023/file/05957c194f4c77ac9d91e1374d2def6b-Paper-Datasets_and_Benchmarks.pdf
  92. https://dl.acm.org/doi/10.1145/3715275.3732186
  93. https://www.sciencedirect.com/science/article/pii/S0010482525004755
  94. https://arxiv.org/abs/2407.19897
  95. https://arxiv.org/html/2310.08537v2
  96. https://www.ijcai.org/proceedings/2023/0747.pdf
  97. https://ieeexplore.ieee.org/document/10193944/
  98. https://github.com/AI4LIFE-GROUP/OpenXAI
  99. https://pmc.ncbi.nlm.nih.gov/articles/PMC12427955/
  100. https://www.researchgate.net/publication/387723318_Explainable_AI_in_Healthcare_Decision_Support_Systems
  101. https://www.sciencedirect.com/science/article/pii/S2001037024002642
  102. https://academic.oup.com/jamia/article/31/4/968/7610153
  103. https://www.sciencedirect.com/science/article/pii/S1532046423000862
  104. https://ai.jmir.org/2024/1/e53207
  105. https://pmc.ncbi.nlm.nih.gov/articles/PMC10148324/
  106. https://www.medrxiv.org/content/10.1101/2024.08.10.24311735v1
  107. https://pmc.ncbi.nlm.nih.gov/articles/PMC10500028/
  108. https://www.sciencedirect.com/science/article/pii/S2352914823001302
  109. https://www.frontiersin.org/journals/radiology/articles/10.3389/fradi.2025.1627169/full
  110. https://www.sciencedirect.com/science/article/abs/pii/S1544612324013874
  111. https://www.researchgate.net/publication/392384304_The_Role_of_Explainable_AI_in_Financial_Risk_Assessment_and_Mitigation
  112. https://easychair.org/publications/preprint/qgpq
  113. https://artificialintelligenceact.eu/high-level-summary/
  114. https://www.smarsh.com/regulations/eu-ai-act
  115. https://amlwatcher.com/blog/explainable-ai-in-aml/
  116. https://www.verint.com/blog/why-are-explainable-ai-and-responsible-ai-important-in-the-financial-compliance-industry/
  117. https://www.bis.org/fsi/fsipapers24.pdf
  118. https://www.researchgate.net/publication/366861518_Explainable_Machine_Learning_for_High-Frequency_Trading_Dynamics_Discovery
  119. https://www.deloitte.com/us/en/insights/industry/financial-services/explainable-ai-in-banking.html
  120. https://link.springer.com/article/10.1007/s10462-024-10854-8
  121. https://www.cambridge.org/core/journals/data-and-policy/article/explainable-and-transparent-artificial-intelligence-for-public-policymaking/51D4C6E27CFDEB3CD19EC5E1A6F4FAE7
  122. https://www.researchgate.net/publication/378273457_Explainable_and_transparent_artificial_intelligence_for_public_policymaking
  123. https://www.sciencedirect.com/science/article/pii/S0740624X24000571
  124. https://www.brookings.edu/articles/the-tensions-between-explainable-ai-and-good-public-policy/
  125. https://ppr.lse.ac.uk/articles/10.31389/lseppr.111
  126. https://dl.acm.org/doi/10.5555/3666122.3667121
  127. https://arxiv.org/abs/2410.23953
  128. https://minesparis-psl.hal.science/hal-04162065v1
  129. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4851102
  130. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_202401689
  131. https://gdpr-info.eu/art-22-gdpr/
  132. https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf
  133. https://www.iso.org/standard/82148.html
  134. https://hyperight.com/role-of-explainability-in-ai-regulatory-frameworks/
  135. https://pmc.ncbi.nlm.nih.gov/articles/PMC11391805/
  136. https://ssir.org/articles/entry/do_ai_systems_need_to_be_explainable
  137. https://arxiv.org/html/2405.03820v1
  138. https://www.dlapiper.com/en-us/insights/publications/2024/05/explainability-of-ai-benefits-risks-and-accountability
  139. https://www.joneswalker.com/en/insights/blogs/ai-law-blog/the-explainability-illusion-why-ai-transparency-requirements-miss-the-point.html?id=102kzle
  140. https://ecnl.org/news/packed-loopholes-why-ai-act-fails-protect-civic-space-and-rule-law
  141. https://www.softwareimprovementgroup.com/the-pros-and-cons-of-explainable-ai/
  142. https://www.wearedevelopers.com/magazine/eu-ai-regulation-artificial-intelligence-regulations
  143. https://artificialintelligenceact.eu/article/13/
  144. https://www.euaiact.com/key-issue/5
  145. https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-21-Accelerating-Federal-Use-of-AI-through-Innovation-Governance-and-Public-Trust.pdf
  146. https://cset.georgetown.edu/wp-content/uploads/t0592_china_ai_law_draft_EN.pdf
  147. https://www.dlapiper.com/en-us/insights/publications/2024/09/china-releases-ai-safety-governance-framework
  148. https://oecd.ai/en/dashboards/ai-principles/P7
  149. https://digital.sandiego.edu/cgi/viewcontent.cgi?article=1366&context=ilj
  150. https://www.tandfonline.com/doi/abs/10.1080/13600834.2025.2537514
  151. https://carnegieendowment.org/research/2024/04/a-global-south-perspective-on-explainable-ai?lang=en
  152. https://www.sciencedirect.com/science/article/pii/S026840122200072X
  153. https://arxiv.org/abs/2010.13764
  154. https://facctconference.org/static/pdfs_2022/facct22-3533090.pdf
  155. https://arxiv.org/abs/1811.10154
  156. https://www.jmlr.org/papers/volume22/19-325/19-325.pdf
  157. https://arxiv.org/pdf/2006.11371
  158. https://massedcompute.com/faq-answers/?question=What%2520are%2520the%2520computational%2520complexities%2520of%2520LIME%2520and%2520SHAP%2520for%2520high-dimensional%2520data?
  159. https://massedcompute.com/faq-answers/?question=Can%2520SHAP%2520and%2520LIME%2520handle%2520high-dimensional%2520input%2520data?
  160. https://arxiv.org/html/2504.20687v1
  161. https://www.sciencedirect.com/science/article/pii/S2666764925000359
  162. https://repo.ijiert.org/index.php/ijiert/article/view/3930/3317
  163. https://arxiv.org/html/2412.14056v1
  164. https://www.sciencedirect.com/science/article/abs/pii/S0306437922001405
  165. https://www.sciencedirect.com/science/article/abs/pii/S1566253524000812
  166. https://arxiv.org/html/2306.06123v4
  167. https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1567
  168. https://www.researchgate.net/publication/391111496_Evaluating_the_Robustness_of_Explainable_AI_Models_Against_Adversarial_Attacks_in_High-Stakes_Domains
  169. https://arxiv.org/abs/2510.03623
  170. https://link.springer.com/article/10.1007/s44204-024-00238-8
  171. https://arxiv.org/abs/2305.17258
  172. https://arxiv.org/abs/2512.18792
  173. https://arxiv.org/abs/2204.08859
  174. https://www.microsoft.com/en-us/research/wp-content/uploads/2022/06/Aether-Overreliance-on-AI-Review-Final-6.21.22.pdf
  175. https://doi.org/10.1145/3287560.3287590
  176. https://www.researchgate.net/publication/396683432_Non-experts%27_Trust_in_XAI_is_Unreasonably_High
  177. https://doi.org/10.1038/s41398-021-01224-x
  178. https://doi.org/10.1109/ICHI.2015.26
  179. https://pmc.ncbi.nlm.nih.gov/articles/PMC11240172/
  180. https://theses.hal.science/tel-04661844v1
  181. https://www.sciencedirect.com/science/article/abs/pii/S000437022300098X
  182. https://www.gartner.com/en/articles/hype-cycle-for-artificial-intelligence
  183. https://ai-techpark.com/superwise-leads-explainable-ai-in-4-gartner-hype-cycles-2025/
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