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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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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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
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
The psychology of interpersonal relations
Heider, F. (1958) · American Sociological Review · doi:10.2307/2089062 · Cited by 17,189
(2011) · Cited by 14,317
“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
(1987) · Medical Entomology and Zoology · Cited by 4,269
Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment.
Tversky, A., & Kahneman, D. (1983) · Psychological Review · doi:10.1037/0033-295x.90.4.293 · Cited by 3,991
Halpern, J. Y. (1994) · Mathematical Social Sciences · doi:10.1016/0165-4896(94)90005-1 · Cited by 3,620
Flagged first, then by citations
2 of the 173 references couldn’t be matched to a known paper.
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
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Items 101–200 of 1,050
Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. G. (2024) · IEEE Access · doi:10.1109/access.2024.3431437 · Cited by 459
Explainable Artificial Intelligence in CyberSecurity: A Survey
Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022) · IEEE Access · doi:10.1109/access.2022.3204171 · Cited by 330
Improved Code Summarization via a Graph Neural Network
LeClair, A., Haque, S., Wu, L., & McMillan, C. (2020) · International Conference on Program Comprehension · doi:10.1145/3387904.3389268 · Cited by 322
Wang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320
Supporting Human-AI Teams:Transparency, explainability, and situation awareness
Endsley, M. R. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107574 · Cited by 366
Explainable artificial intelligence (XAI) in finance: a systematic literature review
Černevičienė, J., & Kabašinskas, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10854-8 · Cited by 507
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Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019) · arXiv · doi:10.48550/arxiv.1905.05134 · Cited by 318
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Ferrer, X., Nuenen, T. V., Such, J. M., Cote, M., & Criado, N. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056293 · Cited by 308
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Chiou, E. K., & Lee, J. D. (2023) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211009995 · Cited by 301
Mukhamediev, R. I., Popova, Y., Kuchin, Y. I., Zaitseva, E., Kalimoldayev, A. M., Сымагулов, А., Levashenko, V., Abdoldina, F., Gopejenko, V. I., Yakunin, K., et al. (2022) · Mathematics · doi:10.3390/math10152552 · Cited by 415
Open Problems in Mechanistic Interpretability
Sharkey, L., Chughtai, B., Batson, J., Lindsey, J., Wu, J., Bushnaq, L., Goldowsky-Dill, N., Heimersheim, S., Ortega, A., Bloom, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.16496 · Cited by 291
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Bjerring, J. C., & Busch, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-019-00391-6 · Cited by 274
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Byrne, R. M. J. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/876 · Cited by 285
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Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302
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Tiddi, I., & Stefan Schlobach, A. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103627 · Cited by 287
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“Combining increased cus-tomizability with metacognitive support strategies (e.g., planning, self-evaluation, self-management) is a promising direction for further research.”
BackgroundBenchmarking and survey of explanation methods for black box models
Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., & Rinzivillo, S. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00933-9 · Cited by 410
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Pham, T. D. (2025) · Royal Society Open Science · doi:10.1098/rsos.241873 · Cited by 367
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Anjomshoae, S., Najjar, A., Calvaresi, D., & Främling, K. (2019) · Adaptive Agents and Multi-Agent Systems · doi:10.65109/kczb5817 · Cited by 402
Algorithmic recourse: From counterfactual explanations to interventions
Karimi, A.-H., Schölkopf, B., & Valera, I. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445899 · Cited by 274
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Puiutta, E., & Veith, E. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57321-8_5 · Cited by 311
Relation between prognostics predictor evaluation metrics and local interpretability SHAP values
Baptista, M. L., Goebel, K., & Henriques, E. M. P. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103667 · Cited by 283
Sokol, K., & Flach, P. A. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372870 · Cited by 278
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Arya, V., Bellamy, R. K. E., Chen, P.-Y., Dhurandhar, A., Hind, M., Hoffman, S. C., Houde, S., Liao, Q. V., Luss, R., Mojsilović, A., et al. (2019) · arXiv · doi:10.48550/arxiv.1909.03012 · Cited by 268
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. S. (2024) · ACM Computing Surveys · doi:10.1145/3677119 · Cited by 723
On the (In)fidelity and Sensitivity for ExplanationsPreprint
Yeh, C., Hsieh, C.-Y., Suggala, A. S., Inouye, D. I., & Ravikumar, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09392 · Cited by 321
The explanation game: Explaining machine learning models using shapley values
Merrick, L., & Taly, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57321-8_2 · Cited by 277
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267
Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
Four principles of explainable artificial intelligence
Phillips, P. J., Hahn, C. A., Fontana, P. C., Yates, A. N., Greene, K., Broniatowski, D. A., & Przybocki, M. A. (2021) · doi:10.6028/nist.ir.8312 · Cited by 262
Trustworthy ai: A computational perspective
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261
van Zyl, C., Ye, X., & Naidoo, R. M. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122079 · Cited by 297
Explaining the black-box model: A survey of local interpretation methods for deep neural networks
Liang, Y., Li, S., Yan, C., Li, M., & Jiang, C. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.011 · Cited by 264
Machine Learning (ML) in Medicine: Review, Applications, and Challenges
Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Naqvi, R. A. (2021) · Mathematics · doi:10.3390/math9222970 · Cited by 289
Amann, J., Vetter, D., Blomberg, S. N., Christensen, H. C., Coffee, M., Gerke, S., Gilbert, T. K., Hagendorff, T., Holm, S., Livne, M., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000016 · Cited by 251
Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach
Ehsan, U., & Riedl, M. O. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60117-1_33 · Cited by 274
Explainable Artificial Intelligence for Predictive Modeling in Healthcare
Yang, C. C. (2022) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-022-00114-1 · Cited by 288
The challenge of crafting intelligible intelligence
Weld, D. S., & Bansal, G. (2019) · Communications of the ACM · doi:10.1145/3282486 · Cited by 253
Neupane, S., Ables, J., Anderson, W. S., Mittal, S., Rahimi, S., Banicescu, I., & Seale, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3216617 · Cited by 293
Grimmelikhuijsen, S. (2023) · Public Administration Review · doi:10.1111/puar.13483 · Cited by 259
A sociotechnical perspective for the future of AI: narratives, inequalities, and human control
Sartori, L., & Theodorou, A. A. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09624-3 · Cited by 310
Shorey, S., Ang, E., Yap, J., Ng, E. D., Lau, S. T., & Chui, C. K. (2019) · Journal of Medical Internet Research · doi:10.2196/14658 · Cited by 238
Causal interpretability for machine learning-problems, methods and evaluation
Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H. (2020) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3400051.3400058 · Cited by 238
Explainable Reinforcement Learning: A Survey and Comparative Review
Milani, S., Topin, N., Veloso, M., & Fang, F. (2023) · ACM Computing Surveys · doi:10.1145/3616864 · Cited by 347
An explainable AI (XAI) model for landslide susceptibility modeling
Pradhan, B., Dikshit, A., Lee, S., & Kim, H. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110324 · Cited by 248
Makumbura, R. K., Mampitiya, L. I., Rathnayake, N., Meddage, D. P. P., Henna, S., Dang, T. L., Hoshino, Y., & Rathnayake, U. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102831 · Cited by 249
Fairness in recommender systems: research landscape and future directions
Deldjoo, Y., Jannach, D., Bellogín, A., Difonzo, A., & Zanzonelli, D. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-023-09364-z · Cited by 290
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
Páez, A. (2019) · Minds and Machines · doi:10.1007/s11023-019-09502-w · Cited by 229
Problems with Shapley-value-based explanations as feature importance measures
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020) · arXiv · doi:10.48550/arxiv.2002.11097 · Cited by 228
“Instead, Miller (2019) argues that humans are selective about explanations: certain contrasts are more meaningful than others.”
BackgroundExplainability in human–agent systems
Rosenfeld, A., & Richardson, A. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09408-y · Cited by 249
Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110786 · Cited by 233
Ghaffarian, S., Taghikhah, F. R., & Maier, H. R. (2023) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2023.104123 · Cited by 269
Bag, S., Srivastava, G., Bashir, M. A., Kumari, S., Giannakis, M., & Chowdhury, A. H. (2021) · Benchmarking An International Journal · doi:10.1108/bij-07-2021-0415 · Cited by 286
Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10223-8 · Cited by 289
Liaw, S. Y., Tan, J. Z., Lim, S., Zhou, W., Yap, J., Ratan, R., Ooi, S. L., Wong, S. J., Seah, B., & Chua, W. L. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105718 · Cited by 223
A survey of visual analytics for Explainable Artificial Intelligence methods
Alicioglu, G., & Sun, B. (2022) · Computers & Graphics · doi:10.1016/j.cag.2021.09.002 · Cited by 221
Artificial intelligence research in hospitality: a state-of-the-art review and future directions
Law, R., Lin, K. J., Ye, H., & Fong, D. K. C. (2023) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-02-2023-0189 · Cited by 234
Human factors in model interpretability: Industry practices, challenges, and needs
Hong, S. R., Hullman, J., & Bertini, E. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392878 · Cited by 219
Naser, M. Z. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103821 · Cited by 228
The three ghosts of medical AI: Can the black-box present deliver?
Quinn, T. P., Jacobs, S., Senadeera, M., Le, V., & Coghlan, S. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102158 · Cited by 246
Counterfactual Explanations for Machine Learning: A Review.Preprint
Verma, S., Dickerson, J. P., & Hines, K. (2020) · arXiv (Cornell University) · Cited by 223
Interactive and Explainable Region-guided Radiology Report Generation
Tanida, T., Müller, P., Kaissis, G. A., & Rueckert, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00718 · Cited by 247
Mueller, S. T., Hoffman, R. R., Clancey, W., Emrey, A., & Klein, G. (2019) · arXiv · doi:10.48550/arxiv.1902.01876 · Cited by 215
Wanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2022) · Electronic Markets · doi:10.1007/s12525-022-00593-5 · Cited by 269
Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103459 · Cited by 211
Criteria for classifying forecasting methods
Januschowski, T., Gasthaus, J. A., Wang, Y., Salinas, D., Flunkert, V., Bohlke‐Schneider, M., & Callot, L. (2019) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.05.008 · Cited by 211
Digital nudging with recommender systems: Survey and future directions
Jesse, M., & Jannach, D. (2021) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2020.100052 · Cited by 222
Post-hoc Interpretability for Neural NLP: A Survey
Madsen, A., Reddy, S., & Chandar, S. (2022) · ACM Computing Surveys · doi:10.1145/3546577 · Cited by 283
Human-centered XAI: Developing design patterns for explanations of clinical decision support systems
Schoonderwoerd, T. A. J., Jorritsma, W., Neerincx, M. A., & van den Bosch, K. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102684 · Cited by 210
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Bharati, S., Mondal, M. R. H., & Podder, P. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3266418 · Cited by 251
Bad machines corrupt good morals
Köbis, N., Bonnefon, J., & Rahwan, I. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01128-2 · Cited by 225
A Review on Explainability in Multimodal Deep Neural Nets
Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204
Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists
Retzlaff, C. O., Angerschmid, A., Saranti, A., Schneeberger, D. M., Röttger, R., Müller, H., & Holzinger, A. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101243 · Cited by 229
Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges
Blasch, E., Pham, T., Chong, C.-Y., Koch, W., Leung, H., Braines, D., & Abdelzaher, T. (2021) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2020.3049030 · Cited by 207
Explainable Recommendation: A Survey and New Perspectives
Zhang, Y., & Chen, X. (2020) · doi:10.1561/9781680836592 · Cited by 215
Esposito, E. (2022) · The MIT Press eBooks · doi:10.7551/mitpress/14189.001.0001 · Cited by 222
Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions
Theissler, A., Spinnato, F., Schlegel, U., & Guidotti, R. (2022) · IEEE Access · doi:10.1109/access.2022.3207765 · Cited by 245
Explainable AI for Bioinformatics: Methods, Tools and Applications
Karim, M. R., Islam, T., Shajalal, M., Beyan, O., Lange, C., Cochez, M., Rebholz‐Schuhmann, D., & Decker, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad236 · Cited by 220
Algorithmic Impact Assessments under the GDPR: Producing Multi-layered Explanations
Kaminski, M. E., & Malgieri, G. (2019) · International Data Privacy Law · doi:10.1093/idpl/ipaa020 · Cited by 207
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022) · Social Science Computer Review · doi:10.1177/0894439320980118 · Cited by 192
“XAI refers to “an explanatory agent revealing underlying causes to its or another agent’s decision making” (Miller, 2019, p. 4).”
BackgroundInfluentialToward personalized XAI: A case study in intelligent tutoring systems
Conati, C., Barral, O., Putnam, V., & Rieger, L. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103503 · Cited by 233
"The human body is a black box": supporting clinical decision-making with deep learning
Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., & O'Brien, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372827 · Cited by 196
Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200
Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt
Wolanin, A., Mateo‐García, G., Camps‐Valls, G., Gómez‐Chova, L., Meroni, M., Duveiller, G., Liangzhi, Y., & Guanter, L. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/ab68ac · Cited by 205
Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review
Frasca, M., La Torre, D., Pravettoni, G., & Cutica, I. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00114-7 · Cited by 190
How do visual explanations foster end users' appropriate trust in machine learning?
Yang, F., Huang, Z., Scholtz, J., & Arendt, D. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377480 · Cited by 200
Advancing Computational Toxicology by Interpretable Machine Learning
Jia, X., Wang, T., & Zhu, H. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c00653 · Cited by 199
Pataranutaporn, P., Liu, R., Finn, E., & Maes, P. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00720-7 · Cited by 192
Explainable AI under Contract and Tort Law: Legal Incentives and Technical Challenges
Hacker, P., Krestel, R., Grundmann, S., & Naumann, F. (2020) · Artificial Intelligence and Law · doi:10.1007/s10506-020-09260-6 · Cited by 204
Sokol, K., & Flach, P. (2020) · KI - Künstliche Intelligenz · doi:10.1007/s13218-020-00637-y · Cited by 186
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI
Tomsett, R., Preece, A., Braines, D., Cerutti, F., Chakraborty, S., Srivastava, M., Pearson, G., & Kaplan, L. (2020) · Patterns · doi:10.1016/j.patter.2020.100049 · Cited by 186
Contrastive explanation: a structural-model approach
Miller, T. (2021) · The Knowledge Engineering Review · doi:10.1017/s0269888921000102 · Cited by 186
Self-citationDing, W., Abdel‐Basset, M., Hawash, H., & Ali, A. M. A. (2022) · Information Sciences · doi:10.1016/j.ins.2022.10.013 · Cited by 214
Grimmelikhuijsen, S., & Meijer, A. (2022) · Perspectives on Public Management and Governance · doi:10.1093/ppmgov/gvac008 · Cited by 189
Retzlaff, C. O., Das, S., Wayllace, C., Mousavi, P., Afshari, M. P., Yang, T., Saranti, A., Angerschmid, A., Taylor, M. E., & Holzinger, A. (2024) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.15348 · Cited by 224
A literature review on applications of explainable artificial intelligence (XAI)
Kalasampath, K., Spoorthi, K. N., Sajeev, S., Kuppa, S. S., Ajay, K., & Maruthamuthu, A. (2025) · IEEE Access · doi:10.1109/access.2025.3546681 · Cited by 180
Explanation in AI and law: Past, present and future
Atkinson, K. M., Bench-Capon, T. J. M., & Bollegala, D. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103387 · Cited by 214
Asatiani, A., Malo, P., Nagbøl, P. R., Penttinen, E., Rinta-Kahila, T., & Salovaara, A. (2021) · Journal of the Association for Information Systems · doi:10.17705/1jais.00664 · Cited by 223
Jacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177
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
About these checks
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