The DOI matches its Crossref record; 171 of the 173 references it cites were found in a registry or an index.
Explanation in artificial intelligence: Insights from the social sciences
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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 check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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 the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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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The self-check needs the paper’s full text.
Citations
Cites
Grice, H. P. (1975) · Speech Acts · doi:10.1163/9789004368811_003 · Cited by 23,221
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
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The psychology of interpersonal relations
Heider, F. (1958) · American Sociological Review · doi:10.2307/2089062 · Cited by 17,189
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“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
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Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment.
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Research works printed but not linked: 5
- Angwin, 2016, Machine bias, ProPublica
- Causation, 1974, J. Philos., 70, 556
- McClure, 2003, The role of goal-based explanations, vol. 5, 306
- Peirce, 1903, Harvard lectures on pragmatism, vol. 5
- Van Fraassen, 1977, The pragmatics of explanation, Am. Philos. Q., 14, 143
Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
“classification or forecast can be complex for AI based systems (Miller, 2019).”
BackgroundInfluentialMachine learning and deep learning
Janiesch, C., Zschech, P., & Heinrich, K. (2021) · Electronic Markets · doi:10.1007/s12525-021-00475-2 · Cited by 2,959
Explainable AI: a review of machine learning interpretability methods
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922
Metadata
- 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
- 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)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- explainable AI, human explanations, cognitive biases, social expectations, explanation evaluation, explanation generation, philosophy of explanation
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