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 901–1,000 of 1,050
When something goes wrong: Who is responsible for errors in ML decision-making?
Berber, A., & Srećković, S. (2023) · AI & Society · doi:10.1007/s00146-023-01640-1 · Cited by 44
Evaluating the impact of human explanation strategies on human-AI visual decision-making
Morrison, K., Shin, D., Holstein, K., & Perer, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579481 · Cited by 37
Zhao, Y., Xiao, L., Liu, Y., Leong, A. T. L., & Wu, E. X. (2023) · NMR in Biomedicine · doi:10.1002/nbm.4956 · Cited by 37
Lee, S., Hong, J., Jeon, G., Jo, J., Boo, S., Kim, H., Jung, S., Park, J., Choi, I., & Kim, S. (2023) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2023.05.014 · Cited by 35
From rules to examples: Machine learning's type of authority
Campolo, A., & Schwerzmann, K. (2023) · Big Data & Society · doi:10.1177/20539517231188725 · Cited by 48
Ridley, M. (2024) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24889 · Cited by 61
Rajpura, P., Cecotti, H., & Meena, Y. K. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad6593 · Cited by 45
Conversational Interfaces for Explainable AI: A Human-Centred Approach
Jentzsch, S. F., Höhn, S., & Hochgeschwender, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30391-4_5 · Cited by 37
Explainable Agents for Less Bias in Human-Agent Decision Making
Malhi, A. K., Knapič, S., & Främling, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-51924-7_8 · Cited by 37
A wrapper methodology to learn interval-valued fuzzy rule-based classification systems
Sanz, J., & Bustince, H. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107249 · Cited by 41
Kempeneer, S. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101578 · Cited by 48
Detecting and explaining unfairness in consumer contracts through memory networks
Ruggeri, F., Lagioia, F., Lippi, M., & Torroni, P. (2021) · Artificial Intelligence and Law · doi:10.1007/s10506-021-09288-2 · Cited by 45
Sestino, A., Peluso, A. M., Amatulli, C., & Guido, G. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.102017 · Cited by 37
van Berkel, N., Bellio, M., Skov, M. B., & BLANDFORD, A. E. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3571815 · Cited by 42
Model Distillation for Revenue Optimization: Interpretable Personalized PricingPreprint
Biggs, M., Sun, W., & Ettl, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01903 · Cited by 40
Kabir, S., Hossain, M. S., & Andersson, K. (2025) · Algorithms · doi:10.3390/a18090556 · Cited by 44
Mitigating belief projection in explainable artificial intelligence via Bayesian teaching
Yang, S. C., Vong, W. K., Sojitra, R. B., Folke, T., & Shafto, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-89267-4 · Cited by 37
Alimi, A. E., Buraimoh, O. F., Aladesusi, G. A., & Babalola, E. O. (2021) · Indonesian Journal of Teaching in Science · doi:10.17509/ijotis.v1i2.38014 · Cited by 45
Moss, L., Corsar, D., Shaw, M., Piper, I., & Hawthorne, C. (2022) · Neurocritical Care · doi:10.1007/s12028-022-01504-4 · Cited by 40
Responsible cognitive digital clones as decision-makers: a design science research study
Golovianko, M., Gryshko, S., Terziyan, V. Y., & Tuunanen, T. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2073278 · Cited by 43
Improving customer satisfaction in proactive service design
Wenninger, A., Rau, D., & Röglinger, M. (2022) · Electronic Markets · doi:10.1007/s12525-022-00565-9 · Cited by 41
ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations
Kim, B., Srinivasan, K. K., Kong, S. H., Kim, J. H., Shin, C. S., & Ram, S. (2023) · MIS Quarterly · doi:10.25300/misq/2022/17141 · Cited by 45
Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network
Nigam, A., Pollice, R., Friederich, P., & Aspuru‐Guzik, A. (2024) · Chemical Science · doi:10.1039/d3sc05306g · Cited by 36
EXMOS: Explanatory Model Steering through Multifaceted Explanations and Data Configurations
Bhattacharya, A., Stumpf, S., Gosak, L., Štiglic, G., & Verbert, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642106 · Cited by 47
Ciatto, G., Sabbatini, F., Agiollo, A., Magnini, M., & Omicini, A. (2024) · ACM Computing Surveys · doi:10.1145/3645103 · Cited by 46
Automated requirement contradiction detection through formal logic and LLMs
Gärtner, A. E., & Göhlich, D. (2024) · Automated Software Engineering · doi:10.1007/s10515-024-00452-x · Cited by 48
Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of Control
Babiker, A., Alshakhsi, S. A. A., Al‐Thani, D., Montag, C., & Ali, R. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2401249 · Cited by 44
To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language Models
Bo, J. Y., Wan, S., & Anderson, A. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714097 · Cited by 36
Bohara, S., Gupta, G., & Kumar, V. (2025) · Journal of Marketing Communications · doi:10.1080/13527266.2025.2508181 · Cited by 42
The effectiveness of explainable AI on human factors in trust models
Cheung, J. C. T., & Ho, S. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-04189-9 · Cited by 36
Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models
Dağlarlı, E. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.92172 · Cited by 38
Bunde, E. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.154 · Cited by 35
Contrastive Explanations of Plans through Model Restrictions
Krarup, B., Krivić, S., Magazzeni, D., Long, D. P., Cashmore, M., & Smith, D. E. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12813 · Cited by 36
Reducing the complexity of chemical networks via interpretable autoencoders
Grassi, T., Nauman, F., Ramsey, J. P., Bovino, S., Picogna, G., & Ercolano, B. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202039956 · Cited by 48
Evans, R., Bošnjak, M., Buesing, L., Ellis, K., Pfau, D., Kohli, P., & Sergot, M. J. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103521 · Cited by 44
Towards Visual Explainable Active Learning for Zero-Shot Classification
Jia, S., Li, Z., Chen, N., & Zhang, J. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114793 · Cited by 39
Sun, X. N., Bahmani, B., Vlassis, N. N., Sun, W., & Xu, Y. (2021) · Granular Matter · doi:10.1007/s10035-021-01137-y · Cited by 38
Zanon, A. L., da Rocha, L. C. D., & Manzato, M. G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109333 · Cited by 36
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and DelayPreprint
Marques-Silva, J. P., Gerspacher, T., Cooper, M., Ignatiev, A., & Narodytska, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.05803 · Cited by 37
Salimparsa, M., Sedig, K., Lizotte, D. J., Abdullah, S. S., Chalabianloo, N., & Muanda, F. T. (2025) · Informatics · doi:10.3390/informatics12040119 · Cited by 39
“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.”
The design of reciprocal learning between human and artificial intelligence
Zagalsky, A., Te'eni, D., Yahav, I., Schwartz, D. G., Silverman, G., Cohen, D., Mann, Y., & Lewinsky, D. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479587 · Cited by 35
Wulff, K., & Finnestrand, H. O. (2023) · AI & Society · doi:10.1007/s00146-023-01633-0 · Cited by 38
Saeidi, V., Seydi, S. T., Kalantar, B., Ueda, N., Tajfirooz, B., & Shabani, F. (2023) · Geomatics Natural Hazards and Risk · doi:10.1080/19475705.2023.2225691 · Cited by 35
Legal implications of automated suspicious transaction monitoring: enhancing integrity of AI
Türkşen, U., Benson, V., & Adamyk, B. (2024) · Journal of Banking Regulation · doi:10.1057/s41261-024-00233-2 · Cited by 53
Masialeti, M., Talaei‐Khoei, A., & Yang, A. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102779 · Cited by 37
Cabitza, F., Fregosi, C., Campagner, A., & Natali, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63803-9_14 · Cited by 41
Mendy, J., Jain, A., & Thomas, A. (2024) · Journal of Managerial Psychology · doi:10.1108/jmp-05-2024-0388 · Cited by 63
Ouifak, H., & Idri, A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130602 · Cited by 39
Explaining Multi-Criteria Decision Aiding Models with an Extended Shapley Value
Labreuche, C., & Fossier, S. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/46 · Cited by 35
Sokol, K., & Flach, P. A. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/836 · Cited by 36
Conversational Interfaces for Information Search
Liao, Q. V., Geyer, W., Muller, M. J., & Khazaen, Y. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-38825-6_13 · Cited by 37
Stepin, I., Alonso, J. M., Catalá, A., & Pereira-Fariña, M. (2020) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz48607.2020.9177629 · Cited by 39
Transparency for Whom? Assessing Discriminatory Artificial Intelligence
van Nuenen, T., Ferrer, X., Such, J. M., & Coté, M. (2020) · Computer · doi:10.1109/mc.2020.3002181 · Cited by 38
Explainability and Interpretability: Keys to Deep Medicine
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-53352-6_1 · Cited by 35
ALICE: Active Learning with Contrastive Natural Language Explanations
Liang, W., Zou, J., & Zhou, Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.355 · Cited by 35
Teaching Responsible Data Science: Charting New Pedagogical Territory
Lewis, A., & Stoyanovich, J. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00241-7 · Cited by 50
Chrysostomou, G., & Αλέτρας, Ν. (2021) · doi:10.18653/v1/2021.acl-long.40 · Cited by 43
Cycle-Consistent Counterfactuals by Latent Transformations
Khorram, S., & Li, F. (2022) · 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/cvpr52688.2022.00996 · Cited by 39
Personalized explanation in machine learning: A conceptualizationPreprint
Schneider, J., & Handali, J. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00770 · Cited by 69
"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep LearningPreprint
Sendak, M., Elish, M. C., Gao, M. C., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., & O’Brien, C. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08089 · Cited by 35
Huang, Y., Wang, C., Wang, Y., Lyu, G., Lin, S., Liu, W., Niu, H., & Hu, Q. (2023) · Frontiers of Environmental Science & Engineering · doi:10.1007/s11783-024-1789-2 · Cited by 34
Shen, X., Liu, H., Qiu, G., Liu, Y., Liu, J., & Fan, S. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3355105 · Cited by 39
Sun, Y., Chen, S., Jiang, H., Qin, B., Li, D., Jia, K., & Wang, C. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.171931 · Cited by 34
Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification
Metta, C., Beretta, A., Guidotti, R., Yin, Y., Gallinari, P., Rinzivillo, S., & Giannotti, F. (2024) · Diagnostics · doi:10.3390/diagnostics14070753 · Cited by 34
Mallick, R., Flathmann, C., Duan, W., Schelble, B. G., & McNeese, N. J. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103355 · Cited by 37
Spatola, N. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100099 · Cited by 55
Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap
Tekkesinoglu, S., Habibovic, A., & Kunze, L. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3714478 · Cited by 46
Tell me a story! Narrative-driven XAI with Large Language Models
Martens, D., Hinns, J., Dams, C., Vergouwen, M., & Evgeniou, T. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114402 · Cited by 68
Memory-Based Explainable Reinforcement Learning
Cruz, F., Dazeley, R., & Vamplew, P. (2019) · Australasian Conference on Artificial Intelligence · doi:10.1007/978-3-030-35288-2_6 · Cited by 36
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario
Cruz, F., Dazeley, R., Vamplew, P., & Moreira, I. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06425-5 · Cited by 34
Hu, H., Wang, D., & Deng, S. (2020) · Journal of Data and Information Science · doi:10.2478/jdis-2020-0027 · Cited by 34
A machine learning approach to estimating the error in satellite sea surface temperature retrievals
Kumar, C. K., Podestá, G., Kilpatrick, K. A., & Minnett, P. J. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112227 · Cited by 41
Beneficial and harmful explanatory machine learning
Ai, L., Muggleton, S., Hocquette, C., Gromowski, M., & Schmid, U. (2021) · Machine Learning · doi:10.1007/s10994-020-05941-0 · Cited by 37
Applications of interpretability in deep learning models for ophthalmology
Hanif, A. M., Beqiri, S., Keane, P. A., & Campbell, J. P. (2021) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000780 · Cited by 36
Herm, L.-V., Janiesch, C., Reijers, H. A., & Seubert, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85469-0_19 · Cited by 34
A survey on effects of adding explanations to recommender systems
Vultureanu‐Albişi, A., & Bădică, C. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6834 · Cited by 37
Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives
Hamberger, M., Ikonomi, N., Schwab, J. D., Werle, S. D., Fürstberger, A., Kestler, A. M. R., Holderried, M., Kaisers, U. X., Steger, F., & Kestler, H. A. (2022) · JMIR mhealth and uhealth · doi:10.2196/32696 · Cited by 38
Borrego-Díaz, J., & Galán-Páez, J. (2022) · Minds and Machines · doi:10.1007/s11023-022-09603-z · Cited by 34
Kim, J., Lee, G., Lee, S., & Lee, C. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121940 · Cited by 37
Data-driven models for predicting community changes in freshwater ecosystems: A review
Lee, D., Lee, D., Cha, Y., Min, J.-H., & Park, Y. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102163 · Cited by 36
Malle, B. F. (2022) · Wiley eBooks · doi:10.1002/9781394266616.ch4 · Cited by 63
Bunn, J. (2020) · Records Management Journal · doi:10.1108/rmj-08-2019-0038 · Cited by 33
Toward a science of human–AI teaming for decision making: a complementarity framework
Gonzalez, C., Donahue, K., Goldstein, D. G., Heidari, H., Jalali, M. S., Schelble, B., Singh, A., & Woolley, A. W. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag030 · Cited by 33
Sargeant, H. (2022) · AI and Ethics · doi:10.1007/s43681-022-00236-7 · Cited by 53
Hernandez-Bocanegra, D. C., & Ziegler, J. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3579541 · Cited by 43
Natural Example-Based Explainability: A Survey
Poché, A., Hervier, L., & Bakkay, M.-C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_2 · Cited by 33
Machine Learning and Health Science Research: Tutorial
Cho, H., She, J., de Marchi, D., El-Zaatari, H., Barnes, E. L., Kahkoska, A. R., Kosorok, M. R., & Virkud, A. (2023) · Journal of Medical Internet Research · doi:10.2196/50890 · Cited by 39
Evans, R. P., Bryant, L. D., Russell, G., & Absolom, K. L. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105342 · Cited by 39
A survey of Internet of medical things: technology, application and future directions
He, P., Huang, D., Wu, D., He, H., Wei, Y., Cui, Y., Wang, R., & Peng, L. (2024) · Digital Communications and Networks · doi:10.1016/j.dcan.2024.11.013 · Cited by 46
Buçinca, Z., Swaroop, S., Paluch, A. E., Doshi‐Velez, F., & Gajos, K. Z. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713229 · Cited by 62
Nicolson, A., Bradburn, E., Gal, Y., Papageorghiou, A. T., & Noble, J. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02023-0 · Cited by 45
Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection
Fauvel, K., Masson, V., Fromont, É., Faverdin, P., & Termier, A. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330712 · Cited by 33
Explanation Perspectives from the Cognitive Sciences---A Survey
Srinivasan, R., & Chander, A. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/670 · Cited by 33
Why bad coffee? Explaining BDI agent behaviour with valuings
Winikoff, M., Sidorenko, G., Dignum, V., & Dignum, F. P. M. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103554 · Cited by 33
Henriksen, A., Enni, S. A., & Bechmann, A. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462564 · Cited by 42
A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods
Vilone, G., & Longo, L. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.717899 · Cited by 38
Explainable predictive modeling for limited spectral data
Akulich, F., Anahideh, H., Sheyyab, M., & Ambre, D. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104572 · Cited by 34
Sufficient Reasons for Classifier Decisions in the Presence of Domain Constraints
Gorji, N., & Rubin, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i5.20507 · Cited by 33
Bennetot, A., Franchi, G., Ser, J. D., Chatila, R., & Díaz-Rodríguez, N. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109947 · Cited by 33
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
