Explanation in artificial intelligence: Insights from the social sciences
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There can be multiple ways of making data and code accessible, and there can be multiple ways to justify a decision and different degrees of transparency in the decision.
“There can be multiple ways of making data and code accessible, and there can be multiple ways to justify a decision and different degrees of transparency in the decision (Miller 2019; Mittelstadt et al. 2019).”
From Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making (2023) · cites this paper as Miller 2019
Section 2.1.5 distinguishes justification from explanation and describes interpretability as a matter of degree; the Introduction also describes multiple approaches to making decisions more understandable. Our copy does not address ways of making data and code accessible, so that part of the sentence is not supported by Miller (2019). (LLM, conf=0.86)
Experimental — accuracy not yet measuredMachine-learning algorithms can be programmed to explain based on what data and which decision rules certain predictions were reached, which is called explainable AI (xAI).
“For instance, machine-learning algorithms can be programmed in a way they can explain based on what data and which decision rules certain predictions were reached, this is called explainable AI (xAI).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Miller 2019
The Introduction describes explaining classifier predictions, and §1.1 defines explainable AI as revealing causes underlying an agent’s decisions. Section 2.6.4 discusses using interpretable models, including decision trees, to explain machine-learning behaviour. These passages support the general idea, but they do not establish that such explanations identify both the data and the decision rules used to reach a particular prediction. (LLM, conf=0.82)
Experimental — accuracy not yet measuredUX in XAI, specifically Human-AI collaborative decision-making, may require more attention than explanation ease of use.
“Also, user experience (UX) in XAI, specifically Human-AI collaborative decision-making, may require more attention than the explanation ease of use [47].”
From Explainable recommendation: when design meets trust calibration (2021) · cites this paper as [47]
Section 5.3.6 (page not provided) argues that XAI has given too little attention to socially interactive explanation and calls for collaboration with HCI researchers. Section 5.3.1 also argues that explanations may need to be interactive. Neither passage specifically prioritizes human–AI collaborative decision-making over explanation ease of use. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSome users prefer a detailed explanation, while others need a brief explanation based on their curiosity level.
“Some users prefer a detailed explanation, while others need a brief explanation based on their curiosity level [47].”
From Explainable recommendation: when design meets trust calibration (2021) · cites this paper as [47]
Section 2.2 identifies curiosity as a reason people ask for explanations, and Section 4.6.1 suggests that users may need less explanation as they become familiar with a system. Neither passage establishes that users prefer detailed or brief explanations according to their curiosity level. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThere is little to no empirical research on the effectiveness of feature importance and counterfactual explanations using Miller's criteria in fostering end-users' trust in AI.
“To the best of our knowledge, there is little to no empirical research on the effectiveness of these two humancentered explanations derived from the literature (i.e., Adadi and Berrada, 2018) using Miller's criteria in fostering end-users' trust in AI.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Miller, 2019
Section 4.6.6 identifies criteria for evaluating explanations—simplicity, generality, and coherence—and discusses their potential role in generating trust. It does not establish the claimed lack of empirical research on feature-importance and counterfactual explanations evaluated using those criteria for end-users’ trust. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Counterfactuals combine Miller's selectivity and contrastivity criteria.
“Counterfactuals combine Miller's selectivity and contrastivity criteria.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Miller, 2019
Section 4.4.1 says that comparing a fact with a foil guides the selection of explanations, supporting the link between contrastivity and selectivity. But Section 2.3 reserves “counterfactual” for a hypothetical change to a cause and calls the contrasting outcome a “foil”; it does not say that counterfactuals generally combine the two criteria. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFeature importance and counterfactual explanations are two promising human-centered post-hoc explanations.
“we identified feature importance explanations and counterfactual explanations as two promising post-hoc explanations for achieving HCXAI.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Miller, 2019
Section 2.3 presents contrastive explanations built around counterfactual outcomes as important for explainable AI, and Section 2.1.5 describes explanation as post-hoc interpretability. Section 4.4 discusses selecting relevant features or causes, but our copy does not identify feature-importance explanations as a post-hoc method. The paper therefore supports the counterfactual side of the cited rationale, not both named approaches. (LLM, conf=0.83)
Experimental — accuracy not yet measuredSocial science literature suggests that natural human explanations are predominantly qualitative [63]-while people may have an intuition about which feature is relevant to the decision, or which feature is more relevant than another, they may not be able to articulate a precise quantification.
“Social science literature suggests that natural human explanations are predominantly qualitative [63]-while people may have an intuition about which feature is relevant to the decision, or which feature is more relevant than another, they may not be able to articulate a precise quantification.”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [63]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredTim Miller argues that AI explanations should be situated within social and human sciences literature on cognition and decision making because XAI problems are fundamentally about relationships between people rather than between people and technology.
“Tim Miller argues for the situation of AI explanations within social and human sciences literature on cognition and decision making, given that the problems XAI addresses are fundamentally those of the relationships between people rather than people and technology [ 43 ].”
From Positioning Paradata: A Conceptual Frame for AI Processual Documentation in Archives and Recordkeeping Contexts (2023) · cites this paper as [43]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredHCI skills such as philosophy, psychology, and cognitive science have proven their worth in XAI by developing explanations that stimulate the human explanation process.
“HCI skills such as philosophy, psychology, and cognitive science have proven their worth in the field of XAI by developing explanations that were able to stimulate the human explanation process [89].”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [89]
The Introduction says research in philosophy, psychology, and cognitive science offers valuable insights into how humans explain, and §5.3.2 describes an interactive XAI system that tailored explanations to users. But the paper does not say these fields have already proven their worth by developing explanations that stimulate the human explanation process: the Introduction says most XAI work has not built on social-science frameworks, and §5.3.6 says recent XAI has not truly adopted socially interactive explanation. (LLM, conf=0.84)
Experimental — accuracy not yet measuredExplanations are selective, meaning that focusing solely on the main causes of a decision-making process is sufficient.
“In addition, they state that explanations are selective , meaning that focusing solely on the main causes of a decision-making process is sufficient.”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [12]
Section 4.4 says explanations select the most relevant causes rather than list every cause. But the paper makes relevance dependent on context and the goal of the explanation (Sections 1.2 and 4.1); it does not say that focusing solely on the main causes of a decision-making process is always sufficient. Page numbers were not provided in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredExplanations that explicitly contrast correct diagnoses against plausible alternatives (contrastive explanations) may be particularly valuable for teaching differential diagnosis skills.
“Explanations that explicitly contrast correct diagnoses against plausible alternatives (contrastive explanations) may be particularly valuable for teaching differential diagnosis skills [159].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [159]
Sections 2.3 and 2.6.2 describe contrastive explanations as explaining why one outcome occurred rather than an alternative. Section 4.2.1 reports support for contrastive explanation in category learning, and Section 4.6.3 gives a glaucoma-diagnosis example. The paper does not establish that such explanations teach differential diagnosis skills or compare correct diagnoses with plausible alternative diagnoses in that teaching context. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThis finding challenges the common belief that counterfactual explanations, which simulate AI reasoning, are easily understandable and enhance trust.
“This finding challenges the common belief that counterfactual explanations, which simulate AI reasoning, are easily understandable and enhance trust [24].”
From AI Trust: Can Explainable AI Enhance Warranted Trust? (2023) · cites this paper as [24]
Section 2.3 describes contrastive explanations using counterfactual cases, and the Introduction describes a general hypothesis that explainable AI can help users understand and trust agents. The paper does not say counterfactual explanations simulate AI reasoning, are easily understandable, or specifically enhance trust; Section 2.3 instead says it is important that the recipient understands the counterfactual case. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredInterventions that cue users to reflect on their goals and strategies via question prompts or conversational interfaces have been shown to improve outcomes in the workplace, education, and other domains.
“Interventions that cue users to reflect on their goals and strategies via question prompts or conversational interfaces have been shown to improve outcomes in the workplace [92,122,209], education [51], and other domains [17,147].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [122]
Section 5.3.2 (Dialogue; page unavailable) describes explanatory dialogue systems that use probing questions, including in intelligent tutoring. It does not show that prompts cue users to reflect on their goals and strategies or that these systems improve outcomes; it says the evaluation of EDGE was anecdotal, with no formal comparison. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFurther complications are introduced when AI systems produce outputs in interaction with humans or other AI or complex sociotechnical systems.
“Moreover, further complications are introduced when AI systems produce outputs in interaction with humans or other AI or complex sociotechnical systems (Borrego-Díaz & Galán-Páez, 2022;Miller, 2019).”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Miller, 2019
Section 5 describes explanation as an interaction between an explainer and an explainee, and §5.3.6 identifies social interaction as a challenge for explainable AI. These passages support the human-interaction part of the claim, but do not address AI systems producing outputs in interaction with other AI or complex sociotechnical systems. The passages provide no page numbers. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMost empirical research on algorithmic transparency has focused on the how, reflected in the explainability, interpretability, and observability of algorithms.
“Most of the empirical research on algorithmic transparency has focused on the how, reflected in the explainability, interpretability and, ultimately, in the observability of algorithms (Miller, 2019;Robert et al., 2020;Shin, 2021;Shin & Park, 2019).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Miller, 2019
The Abstract says that much explainable-AI research seeks algorithmic transparency by explaining decisions or actions, and §2.1.5 equates interpretability with explainability. The paper does not establish that most empirical research on algorithmic transparency focuses on the “how,” or identify observability as its ultimate focus. Page numbers are not available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredxAI refers to the explainability of specific decisions and can also refer to how a model works and develops.
“xAI refers to the explainability of specific decisions, but can also refer to how a model works and develops (e.g., Miller 2019).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Miller 2019
Feature importance explanations satisfy the selectivity criterion.
“Such explanations thus satisfy the selectivity criterion proposed by Miller (2019) because they show how certain factors influenced a decision.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Miller, 2019
Studies show that many XAI methods are designed according to developers' intuition of what constitutes a 'good' explanation, rather than actual insights into users' needs.
“However, studies show that many XAI methods are designed according to developers' intuition of what constitutes a 'good' explanation, rather than actual insights into users' needs [73].”
From Explainable AI for Clinical Decision Support Systems: Literature Review, Key Gaps, and Research Synthesis (2025) · cites this paper as [73]
Much of the literature still speaks in subjective terms of 'good' or 'quality' explanations, often without measuring how well those explanations integrate into clinicians' decision-making processes.
“Much of the literature still speaks in subjective terms of 'good' or 'quality' explanations [73], often without measuring how well those explanations integrate into clinicians' decision-making processes.”
From Explainable AI for Clinical Decision Support Systems: Literature Review, Key Gaps, and Research Synthesis (2025) · cites this paper as [73]
The human trait of explaining the underlying reasoning behind a decision and applying this same logic and requirement in the context of AI is complex.
“The human trait of explaining the underlying reasoning behind a decision and applying this same logic and requirement in the context of AI is complex (Miller 2019).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Miller 2019
Explanations are better when constrictive, meaning that a prerequisite for a good explanation is that it does not only indicate why the model made a decision X, but also why it made decision X rather than decision Y.
“First, explanations are better when constrictive , meaning that a prerequisite for a good explanation is that it does not only indicate why the model made a decision X, but also why it made decision X rather than decision Y.”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [12]
Probabilities are not as important as causal links in order to provide a satisfying explanation.
“It is also explained that probabilities are not as important as causal links in order to provide a satisfying explanation.”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [12]
The concept of what defines a good explanation is not trivial and is widely studied in the literature.
“The concept of what defines a good explanation is not trivial and is an aspect that is widely studied in the literature (Miller, 2019; Holzinger et al., 2020).”
From Understanding user mental models in AI-driven code completion tools: Insights from an elicitation study (2025) · cites this paper as Miller, 2019
Good explanations are contrastive: people do not ask why event P happened, but rather why event P happened instead of some event Q.
“In an important contribution, Miller (2019) emphasises the importance of respecting explanatory practices as people experience them-in particular that good explanations are contrastive, i.e. that”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Miller, 2019, p. 3
Providing explanations has social functions: building or maintaining interpersonal relationships, creating shared meaning, and supporting actions or societal practices.
“social functions have to do with building or maintaining interpersonal relationships, creating shared meaning (Miller, 2019), and with supporting actions or societal practices (e.g.excusing or blaming others, or responding to decisions that affect one) (Mittelstadt et al., 2019;Wachter et al., 2018).”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Miller, 2019
The rapid growth of AI has accelerated discoveries across diverse scientific fields and affected every work environment and is reshaping the labor market with unprecedented speed and scale, with 40% of the global workforce expected to require AI.
“The rapid growth of AI has accelerated discoveries across diverse scientific fields and affected every work environment [62–65] and is reshaping the labor market with unprecedented speed and scale, with 40% of the global workforce expected to require AI, enhancing the need for significant AI upskilling or reskilling [66].”
From Use of artificial intelligence in critical care: opportunities and obstacles (2024) · cites this paper as [63]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredCreating AI with the ability to explain and justify its decision-making could increase end-users' willingness to accept AI recommendations.
“Also, creating AI with the ability to explain and justify its decision-making process [5,38,44] could increase the end-users willingness to accept the AI's recommendations [53,55].”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [38]
In three studies [67], [128], [137], the classifiers used in the experiments are not specified.
“In three studies [67], [128], [137], the classifiers used in the experiments are not specified.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [137]
Counterfactual explanations, as a mixture of tabular and visual output representations, appear also in an augmented reality framework.
“Contfactual explanations, as a mixture of tabular and visual output representations, appear also in an augmented reality framework [137].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [137]
The two aforementioned studies [128], [137] lack an evaluation of the quality of the generated counterfactual explanations themselves.
“However, the two aforementioned studies [128], [137] lack an evaluation of the quality of the generated contfactual explanations themselves.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [137]
According to Miller, a good explanation is contrastive, selective, and truthful.
“Miller argues that a good explanation is contrastive, selective, and truthful.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [7]
Some research asserts that case-based explanations are the most intuitive for human users to comprehend.
“However, some research asserts that this approach is the most intuitive for human users to comprehend [7].”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [7]
Humans tend to devalue explanations that contradict their prior beliefs.
“because humans tend to devalue explanations that contradict their prior beliefs.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [7]
Counterfactual explanations are intuitively understandable for humans.
“which are not only intuitively understandable for humans [7]”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [7]
Generative AI can provide personalized learning plans and tools based on students' individual needs and learning styles to improve learning efficiency and effectiveness.
“Generative AI can provide personalized learning plans and tools based on students' individual needs and learning styles to improve learning efficiency and effectiveness.”
From Generative artificial intelligence empowers educational reform: current status, issues, and prospects (2023) · cites this paper as Miller, 2017
Personalized learning provided by generative AI can lead to unfair outcomes, as different students may face different opportunities and challenges.
“However, this personalized learning can also lead to unfair outcomes, as different students may face different opportunities and challenges.”
From Generative artificial intelligence empowers educational reform: current status, issues, and prospects (2023) · cites this paper as Miller, 2017
If generative AI models only focus on students' individual needs and interests and ignore important learning objectives and standards, students' knowledge and skills may be less comprehensive and in-depth.
“In addition, if generative AI models only focus on students' individual needs and interests, and ignore some important learning objectives and standards, this may also lead to students' knowledge and skills being less comprehensive and in-depth (Miller, 2017).”
From Generative artificial intelligence empowers educational reform: current status, issues, and prospects (2023) · cites this paper as Miller, 2017
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Shapley additive explanations for NO2 forecasting
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- Authors
- Miller, T.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Miller, Tim
- ORCID
- Miller, Tim
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 267
- Pages
- 1-38
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2670253439
- Cited by
- 5,302
- References
- 173
- Citations by year
- 2026: 848 · 2025: 855 · 2024: 765 · 2023: 788 · 2022: 673 · 2021: 690 · 2020: 413 · 2019: 212 · 2018: 51 · 2017: 4 · 2012: 2
- FWCI
- 192.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1461 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council (DP160104083)
- Defence Science and Technology Group
- Defence Science Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- explainable AI, human explanations, cognitive biases, social expectations, explanation evaluation, explanation generation, philosophy of explanation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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