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
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
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
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
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
Items 301–400 of 1,050
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
Cheng, F., Ming, Y., & Qu, H. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030342 · Cited by 137
Selten, F., Robeer, M., & Grimmelikhuijsen, S. (2023) · Public Administration Review · doi:10.1111/puar.13602 · Cited by 148
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.-H., Muller, M., & Riedl, M. O. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642474 · Cited by 123
Reviewing the Need for Explainable Artificial Intelligence (xAI)
Gerlings, J., Shollo, A., & Constantiou, I. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.156 · Cited by 165
Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media
Reviglio, U., & Agosti, C. (2020) · Social Media + Society · doi:10.1177/2056305120915613 · Cited by 172
Levels of explainable artificial intelligence for human-aligned conversational explanations
Dazeley, R., Vamplew, P. W., Foale, C., Young, C., Aryal, S., & Cruz, F. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103525 · Cited by 138
Park, H., Ahn, D., Hosanagar, K., & Lee, J. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445304 · Cited by 152
Human-XAI Interaction: A Review and Design Principles for Explanation User Interfaces
Chromik, M., & Butz, A. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85616-8_36 · Cited by 147
Explaining by Removing: A Unified Framework for Model ExplanationPreprint
Covert, I., Lundberg, S., & Lee, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.14878 · Cited by 162
Efficient semantic segmentation with pyramidal fusion
Oršić, M., & Šegvić, S. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107611 · Cited by 126
On the Explainability of Natural Language Processing Deep Models
Zini, J. E., & Awad, M. (2022) · ACM Computing Surveys · doi:10.1145/3529755 · Cited by 153
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
Liao, Q. V., & Varshney, K. R. (2021) · arXiv · doi:10.48550/arxiv.2110.10790 · Cited by 120
“Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design
Weitz, K., Schiller, D., Schlagowski, R., Huber, T., & André, E. (2020) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-020-00332-0 · Cited by 140
Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems
Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., Grimminger, A., Hammer, B., Haeb‐Umbach, R., et al. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3044366 · Cited by 146
Explainable artificial intelligence for education and training
Fiok, K., Farahani, F. V., Karwowski, W., & Ahram, T. (2021) · The Journal of Defense Modeling and Simulation Applications Methodology Technology · doi:10.1177/15485129211028651 · Cited by 137
Measuring Association Between Labels and Free-Text Rationales
Wiegreffe, S., Marasović, A., & Smith, N. A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.804 · Cited by 119
Sanneman, L., & Shah, J. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2081282 · Cited by 118
Human-Centered Explainable AI (HCXAI): Beyond Opening the Black-Box of AI
Ehsan, U., Wintersberger, P., Liao, Q. V., Watkins, E. A., Manger, C., Daumé III, H., Riener, A., & Riedl, M. O. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503727 · Cited by 117
Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445088 · Cited by 117
A Situation Awareness Perspective on Human-AI Interaction: Tensions and Opportunities
Jiang, J., Karran, A. J., Coursaris, C. K., Léger, P., & Beringer, J. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2093863 · Cited by 140
Chen, V., Yang, M., Cui, W., Kim, J. S., Talwalkar, A., & Ma, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02359-7 · Cited by 144
Dhanorkar, S., Wolf, C. T., Qian, K., Xu, A., Popa, L., & Li, Y. (2021) · Designing Interactive Systems Conference 2021 · doi:10.1145/3461778.3462131 · Cited by 119
Explainability as a Non-Functional Requirement
Köhl, M. A., Baum, K. L., Langer, M., Oster, D., Speith, T., & Bohlender, D. (2019) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2019.00046 · Cited by 132
The black box problem revisited. Real and imaginary challenges for automated legal decision making
Brożek, B., Furman, M., Jakubiec, M., & Kucharzyk, B. (2023) · Artificial Intelligence and Law · doi:10.1007/s10506-023-09356-9 · Cited by 177
MLOps: A Taxonomy and a Methodology
Testi, M., Ballabio, M., Frontoni, E., Iannello, G., Moccia, S., Soda, P., & Vessio, G. (2022) · IEEE Access · doi:10.1109/access.2022.3181730 · Cited by 156
AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors
Kővári, A. (2024) · Information · doi:10.3390/info15110725 · Cited by 175
Houssein, E. H., Gamal, A. M., Younis, E. M. G., & Mohamed, E. (2025) · Cluster Computing · doi:10.1007/s10586-025-05281-5 · Cited by 148
Practical early prediction of students’ performance using machine learning and eXplainable AI
Jang, Y., Choi, S., Jung, H., & Kim, H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11120-6 · Cited by 114
Mertes, S., Huber, T., Weitz, K., Heimerl, A., & André, E. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.825565 · Cited by 119
Chan, V. C. H., Ross, G. B., Clouthier, A. L., Fischer, S. L., & Graham, R. B. (2021) · Applied Ergonomics · doi:10.1016/j.apergo.2021.103574 · Cited by 115
Application of machine learning approaches in supporting irrigation decision making: A review
Umutoni, L., & Samadi, V. S. (2024) · Agricultural Water Management · doi:10.1016/j.agwat.2024.108710 · Cited by 151
Dikmen, M., & Burns, C. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102792 · Cited by 116
Human Evaluation of Models Built for Interpretability
Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S. J., & Doshi‐Velez, F. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5280 · Cited by 114
Confalonieri, R., Weyde, T., Besold, T. R., & Martı́n, F. M. D. P. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103471 · Cited by 112
Laux, J. M., Wachter, S., & Mittelstadt, B. (2024) · Computer law & security review · doi:10.1016/j.clsr.2024.105957 · Cited by 113
Čyras, K., Rago, A., Albini, E., Baroni, P., & Toni, F. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/600 · Cited by 116
Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue
Chazette, L., Brunotte, W., & Speith, T. (2021) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re51729.2021.00025 · Cited by 142
XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
Lopes, P., Silva, E., Braga, C., Oliveira, T., & Rosado, L. (2022) · Applied Sciences · doi:10.3390/app12199423 · Cited by 112
Fairness and accountability of AI in disaster risk management: Opportunities and challenges
Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021) · Patterns · doi:10.1016/j.patter.2021.100363 · Cited by 127
Sherkatghanad, Z., Abdar, M., Charlier, J., & Makarenkov, V. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad131 · Cited by 123
Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions
Hossain, M. I., Zamzmi, G., Mouton, P. R., Salekin, M. S., Sun, Y., & Goldgof, D. B. (2023) · ACM Computing Surveys · doi:10.1145/3637487 · Cited by 135
La Torre, D., Colapinto, C., Durosini, I., & Triberti, S. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3077195 · Cited by 110
Singh, R. K., Pandey, R., & Babu, R. N. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05636-6 · Cited by 120
A survey on interpretable reinforcement learning
Glanois, C., Weng, P., Zimmer, M., Li, D., Yang, T., Hao, J., & Liu, W. (2024) · Machine Learning · doi:10.1007/s10994-024-06543-w · Cited by 176
Quality 4.0 – a review of and framework for quality management in the digital era
Kumar, R. R., Ganesh, L. S., & Rajendran, C. (2021) · International Journal of Quality & Reliability Management · doi:10.1108/ijqrm-05-2021-0150 · Cited by 109
Liu, X., Liu, T., & Feng, P. (2022) · Composite Structures · doi:10.1016/j.compstruct.2022.115184 · Cited by 109
Supporting Artificial Social Intelligence With Theory of Mind
Williams, J., Fiore, S. M., & Jentsch, F. G. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.750763 · Cited by 127
Artificial intelligence and explanation: How, why, and when to explain black boxes
Marcus, E., & Teuwen, J. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111393 · Cited by 114
Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness
chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2025) · ACM Computing Surveys · doi:10.1145/3675392 · Cited by 108
Explainable AI for earth observation: A review including societal and regulatory perspectives
Gevaert, C. M. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102869 · Cited by 128
A Perspective on Explanations of Molecular Prediction Models
Wellawatte, G. P., Gandhi, H. A., Seshadri, A., & White, A. D. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01235 · Cited by 119
Starke, C., & Lünich, M. (2020) · Data & Policy · doi:10.1017/dap.2020.19 · Cited by 107
Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems
Nourani, M., Roy, C., Block, J. E., Honeycutt, D. R., Rahman, T., Ragan, E., & Gogate, V. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450639 · Cited by 107
Opportunities and Risks for Citizen Science in the Age of Artificial Intelligence
Ceccaroni, L., Bibby, J., Roger, E., Flemons, P. K. J., Michael, K., Fagan, L. L., & Oliver, J. L. (2019) · Citizen Science Theory and Practice · doi:10.5334/cstp.241 · Cited by 107
Operationalizing Human-Centered Perspectives in Explainable AI
Ehsan, U., Wintersberger, P., Liao, Q. V., Mara, M., Streit, M., Wachter, S., Riener, A., & Riedl, M. O. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3441342 · Cited by 121
HUMAN‐CENTERED DESIGN OF ARTIFICIAL INTELLIGENCE
Margetis, G., Ntoa, S., Antona, M., & Stephanidis, C. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch42 · Cited by 113
Does Explainable Artificial Intelligence Improve Human Decision-Making?
Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y., & Kantarcioglu, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16819 · Cited by 106
Towards an interpretable deep learning model for mobile malware detection and family identification
Iadarola, G., Martinelli, F., Mercaldo, F., & Santone, A. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102198 · Cited by 106
Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)
Mehta, H., & Passi, K. (2022) · Algorithms · doi:10.3390/a15080291 · Cited by 113
Instance-Based Counterfactual Explanations for Time Series Classification
Delaney, E. D., Greene, D., & Keane, M. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86957-1_3 · Cited by 109
Barda, A. J., Horvat, C. M., & Hochheiser, H. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01276-x · Cited by 105
Silva, A., Schrum, M., Hedlund-Botti, E., Gopalan, N., & Gombolay, M. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2101698 · Cited by 104
Schlicker, N., Langer, M., Ötting, S. K., Baum, K., König, C. J., & Wallach, D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106837 · Cited by 106
Sequeira, P., & Gervasio, M. T. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103367 · Cited by 116
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
Kenny, E. M., & Keane, M. T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17377 · Cited by 108
Artificial intelligence-enabled decision support in nephrology
Loftus, T. J., Shickel, B., Ozrazgat‐Baslanti, T., Ren, Y., Glicksberg, B. S., Cao, J., Singh, K., Chan, L., Nadkarni, G. N., & Bihorac, A. (2022) · Nature Reviews Nephrology · doi:10.1038/s41581-022-00562-3 · Cited by 115
Taxonomy of Trust-Relevant Failures and Mitigation Strategies
Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T. M., Dixon, C., & Tielman, M. L. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374793 · Cited by 103
From Responsibility to Reason-Giving Explainable Artificial Intelligence
Baum, K., Mantel, S., Schmidt, E., & Speith, T. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00510-w · Cited by 103
Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 103
Mylrea, M., & Robinson, N. (2023) · Entropy · doi:10.3390/e25101429 · Cited by 127
The role of domain expertise in trusting and following explainable AI decision support systems
Bayer, S., Gimpel, H., & Markgraf, M. (2022) · Journal of Decision Systems · doi:10.1080/12460125.2021.1958505 · Cited by 103
Cappelen, H., & Dever, J. (2021) · doi:10.1093/oso/9780192894724.001.0001 · Cited by 102
An explainable artificial intelligence approach for financial distress prediction
Zhang, Z., Wu, C., Qu, S., & Chen, X. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102988 · Cited by 122
Cognitive psychology-based artificial intelligence review
Zhao, J. G., Wu, M., Zhou, L., Wang, X., & Jia, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1024316 · Cited by 100
Park, K., & Yoon, H. Y. (2024) · Public Relations Review · doi:10.1016/j.pubrev.2024.102507 · Cited by 137
Principles and Practice of Explainable Machine Learning
Belle, V., & Papantonis, I. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.688969 · Cited by 99
Dignum, V., Baldoni, M., Cristina Baroglio, A., Caon, M., Chatila, R., Dennis, L. A., Génova, G., Haim, G., Kließ, M. S., López-Sánchez, M., et al. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278745 · Cited by 99
Evaluating local explanation methods on ground truth
Guidotti, R. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103428 · Cited by 99
Meddage, D. P. P., Ekanayake, I. U., Weerasuriya, A. U., Lewangamage, C., Tse, K. T., Miyanawala, T. P., & Ramanayaka, C. E. (2022) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2022.105027 · Cited by 103
A Survey of Mental Modeling Techniques in Human–Robot Teaming
Tabrez, A., Luebbers, M. B., & Hayes, B. (2020) · Current Robotics Reports · doi:10.1007/s43154-020-00019-0 · Cited by 98
Deep learning technology for construction machinery and robotics
You, K., Zhou, C., & Ding, L. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104852 · Cited by 104
A Comprehensive Review of Explainable Artificial Intelligence (XAI) in Computer Vision
Cheng, Z. Q., Wu, Y. P., Li, Y., Cai, L., & Ihnaini, B. (2025) · Sensors · doi:10.3390/s25134166 · Cited by 122
Key Questions for Next-Generation Biomonitoring
Makiola, A., Compson, Z. G., Baird, D. J., Barnes, M. A., Boerlijst, S. P., Bouchez, A., Brennan, G. L., Bush, A., Canard, E., Cordier, T., et al. (2020) · Frontiers in Environmental Science · doi:10.3389/fenvs.2019.00197 · Cited by 100
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102607 · Cited by 106
Delivering Trustworthy AI through Formal XAI
Marques-Silva, J. P., & Ignatiev, A. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21499 · Cited by 115
Explainable Artificial Intelligence Approaches: A SurveyPreprint
Islam, S. R., Eberle, W., Ghafoor, S., & Ahmed, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09429 · Cited by 168
COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Abdul, A., von der Weth, C., Kankanhalli, M., & Lim, B. Y. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376615 · Cited by 96
Empowering local communities using artificial intelligence
Hsu, Y.-C., Huang, T.-H., Verma, H., Mauri, A., Nourbakhsh, I., & Bozzon, A. (2022) · Patterns · doi:10.1016/j.patter.2022.100449 · Cited by 96
Incorporating Priors with Feature Attribution on Text Classification
Liu, F., & Avci, B. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1631 · Cited by 120
Evolution, challenges, and application of intelligent ICT education: An overview
Haldorai, A., Murugan, S., & Ramu, A. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22217 · Cited by 95
Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit
Deshmukh, F., & Merchant, S. S. (2020) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000000632 · Cited by 95
Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence
Nußberger, A.-M., Luo, L., Celis, L. E., & Crockett, M. J. (2022) · Nature Communications · doi:10.1038/s41467-022-33417-3 · Cited by 105
Sahoh, B., & Choksuriwong, A. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04594-w · Cited by 111
Secure and Trustworthy Artificial Intelligence-extended Reality (AI-XR) for Metaverses
Qayyum, A., Butt, M. A., Ali, H. M., Usman, M., Halabi, O., Al‐Fuqaha, A., Abbasi, Q. H., Imran, M. A., & Qadir, J. (2023) · ACM Computing Surveys · doi:10.1145/3614426 · Cited by 117
Artificial intelligence in government: Concepts, standards, and a unified framework
Straub, V. J., Morgan, D., Bright, J., & Margetts, H. Z. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101881 · Cited by 154
ViCE: visual counterfactual explanations for machine learning models
Gomez, O., Holter, S., Yuan, J., & Bertini, E. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377536 · Cited by 91
Tackling COVID-19 Through Responsible AI Innovation: Five Steps in the Right Direction
Leslie, D. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.4bb9d7a7 · Cited by 103
Kalakoti, R., Bahsi, H., & Nõmm, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3360626 · Cited by 93
Exploiting Explanations for Model Inversion Attacks
Zhao, X., Zhang, W., Xiao, X., & Lim, B. Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00072 · Cited by 109
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
