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
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
Cited by
Sorted by citations
Items 401–500 of 1,050
Okolo, C. T., Kamath, S., Dell, N. L., & Vashistha, A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445420 · Cited by 100
Explainable Embodied Agents Through Social Cues
Wallkötter, S., Tulli, S., Castellano, G., Paiva, A., & Chétouani, M. (2021) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3457188 · Cited by 100
Brasse, J., Broder, H. R., Förster, M., Klier, M., & Sigler, I. (2023) · Electronic Markets · doi:10.1007/s12525-023-00644-5 · Cited by 121
Using Perceptual and Cognitive Explanations for Enhanced Human-Agent Team Performance
Neerincx, M. A., van der Waa, J., Kaptein, F., & van Diggelen, J. (2018) · Interacción · doi:10.1007/978-3-319-91122-9_18 · Cited by 88
People's Explanations of Robot Behavior Subtly Reveal Mental State Inferences
de Graaf, M. M. A., & Malle, B. F. (2019) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2019.8673308 · Cited by 100
Felzmann, H., Fosch‐Villaronga, E., Lutz, C., & Tamò‐Larrieux, A. (2019) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2019.2904644 · Cited by 90
Explaining Answers with Entailment Trees
Dalvi, B. B., Jansen, P. A., Tafjord, O., Xie, Z., Smith, H., Pipatanangkura, L., & Clark, P. E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.585 · Cited by 148
Explainable deep learning in plant phenotyping
Mostafa, S., Mondal, D., Panjvani, K., Kochian, L. V., & Stavness, I. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1203546 · Cited by 99
Hooshyar, D., & Yang, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3463948 · Cited by 108
A review of explainable artificial intelligence in smart manufacturing
Madathil, A. P., Luo, X., Liu, Q., Walker, C. T., Madarkar, R., & Qin, Y. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2513574 · Cited by 121
Explainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269
Explainable autonomous robots: a survey and perspective
Sakai, T., & Nagai, T. (2022) · Advanced Robotics · doi:10.1080/01691864.2022.2029720 · Cited by 101
Guan, Y., Ren, Y., Sun, Q., Li, S. E., Ma, H., Duan, J., Dai, Y., & Cheng, B. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3163816 · Cited by 90
Hakkoum, H., Idri, A., & Abnane, I. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107829 · Cited by 98
Interpretable deep learning approach for tool wear monitoring in high-speed milling
Guo, H., Zhang, Y., & Zhu, K. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103638 · Cited by 93
Cabitza, F., Campagner, A., Malgieri, G., Natali, C., Schneeberger, D., Stoeger, K., & Holzinger, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118888 · Cited by 89
Counterfactual Explanations for Multivariate Time Series
Ates, E., Aksar, B., Leung, V. J., & Coskun, A. K. (2021) · 2021 International Conference on Applied Artificial Intelligence (ICAPAI) · doi:10.1109/icapai49758.2021.9462056 · Cited by 88
Explainable image classification with evidence counterfactual
Vermeire, T., Brughmans, D., Goethals, S., Oliveira, R., & Martens, D. (2022) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01055-y · Cited by 102
Triberti, S., Durosini, I., & Pravettoni, G. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00117 · Cited by 98
Champendal, M., Müller, H., Prior, J. O., & dos Reis, C. S. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111159 · Cited by 104
Morichetta, A., Casas, P., & Mellia, M. (2019) · doi:10.1145/3359992.3366639 · Cited by 87
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals
Galhotra, S., Pradhan, R., & Salimi, B. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3458455 · Cited by 95
Visual interpretability in 3D brain tumor segmentation network
Saleem, H. A., Shahid, A. R., & Raza, B. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104410 · Cited by 92
Abedin, B. (2021) · Internet Research · doi:10.1108/intr-05-2020-0300 · Cited by 114
Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers
Tsai, C.-H., You, Y., Gui, X., Kou, Y., & Carroll, J. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445101 · Cited by 86
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Ross, A., Marasović, A., & Peters, M. E. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.336 · Cited by 124
GraphSVX: Shapley Value Explanations for Graph Neural Networks
Duval, A., & Malliaros, F. D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86520-7_19 · Cited by 91
Explainable Machine Learning for Property Predictions in Compound Optimization
Rodríguez-Pérez, R., & Bajorath, J. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01789 · Cited by 88
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Ehsan, U., Saha, K., Choudhury, M. D., & Riedl, M. O. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579467 · Cited by 113
Artificial intelligence for clinical decision support in neurology
Pedersen, M., Verspoor, K. M., Jenkinson, M., Law, M., Abbott, D. F., & Jackson, G. D. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa096 · Cited by 94
The European Legal Framework for Medical AI
Schneeberger, D., Stöger, K., & Holzinger, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-57321-8_12 · Cited by 85
Explainable Artificial Intelligence (XAI)
Ridley, M. (2022) · Information Technology and Libraries · doi:10.6017/ital.v41i2.14683 · Cited by 115
Saheb, T. (2022) · AI and Ethics · doi:10.1007/s43681-022-00196-y · Cited by 106
Edge Intelligence—Research Opportunities for Distributed Computing Continuum Systems
Pujol, V. C., Donta, P. K., Morichetta, A., Murturi, I., & Dustdar, S. (2023) · IEEE Internet Computing · doi:10.1109/mic.2023.3284693 · Cited by 89
Berman, A., Licht, K. D. F., & Carlsson, V. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102471 · Cited by 137
Trustworthy AI: Securing Sensitive Data in Large Language Models
Feretzakis, G., & Verykios, V. S. (2024) · AI · doi:10.3390/ai5040134 · Cited by 102
Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., Bluemke, E., Rajkumar, N., Krueger, D., Kolt, N., et al. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658948 · Cited by 84
Keane, M. T., & Kenny, E. M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-29249-2_11 · Cited by 84
AI ‘Everywhere and Nowhere’: Addressing the AI Intelligibility Problem in Public Service Journalism
Jones, B., Jones, R., & Luger, E. (2022) · Digital Journalism · doi:10.1080/21670811.2022.2145328 · Cited by 84
Enhance explainability of manifold learning
Han, H., Li, W., Wang, J., Qin, G., & Qin, X. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.119 · Cited by 83
Dash, B., & Sharma, P. K. (2022) · Academic Journal of Research and Scientific Publishing · doi:10.52132/ajrsp.e.2022.39.4 · Cited by 88
Rad, H. S. (2024) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2024.2309539 · Cited by 84
Contrastive Explanations for Model Interpretability
Jacovi, A., Swayamdipta, S., Ravfogel, S., Elazar, Y., Choi, Y., & Goldberg, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.120 · Cited by 83
Varieties of AI Explanations Under the Law. From the GDPR to the AIA, and Beyond
Hacker, P., & Passoth, J.-H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_17 · Cited by 96
Banerjee, S., Alsop, P. H., Jones, L. A., & Cardinal, R. N. (2022) · Patterns · doi:10.1016/j.patter.2022.100506 · Cited by 86
Kim, D., Song, Y., Kim, S., Lee, S., Wu, Y., Shin, J., & Lee, D. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122343 · Cited by 99
Fairness issues, current approaches, and challenges in machine learning models
Jui, T. D., & Rivas, P. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02083-2 · Cited by 100
Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
Wang, Q., Huang, K., Chandak, P., Zitnik, M., & Gehlenborg, N. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209435 · Cited by 82
Erasmus, A., Brunet, T. D. P., & Fisher, E. (2020) · Philosophy & Technology · doi:10.1007/s13347-020-00435-2 · Cited by 92
Toward an End-to-End Sociology of 21st-Century Machine Learning
Roberge, J., & Castelle, M. (2020) · Springer eBooks · doi:10.1007/978-3-030-56286-1_1 · Cited by 81
Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty
Jiang, J., Kahai, S. S., & Yang, M. H. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102839 · Cited by 92
Goodwin, N. L., Nilsson, S. R. O., Choong, J. J., & Golden, S. A. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102544 · Cited by 85
Ethics of Artificial Intelligence
Rios-Campos, C., Tejada-Castro, M. I., Viteri, J. D. C. L., Zambrano, E. O. G., Núñez, J. B., & Vara, F. E. O. (2023) · SpringerBriefs in Research and Innovation Governance · doi:10.1007/978-3-031-17040-9 · Cited by 40
Fairness Perceptions of Artificial Intelligence: A Review and Path Forward
Narayanan, D., Nagpal, M., McGuire, J., Schweitzer, S., & Cremer, D. D. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210890 · Cited by 99
Fostering Collective Intelligence in Human–AI Collaboration: Laying the Groundwork for COHUMAIN
Gupta, P., Nguyen, T. N., González, C., & Woolley, A. W. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12679 · Cited by 108
Human Autonomy at Risk? An Analysis of the Challenges from AI
Prunkl, C. (2024) · Minds and Machines · doi:10.1007/s11023-024-09665-1 · Cited by 105
On cognitive preferences and the plausibility of rule-based models
Fürnkranz, J., Kliegr, T., & Paulheim, H. (2019) · Machine Learning · doi:10.1007/s10994-019-05856-5 · Cited by 96
Learning With Interpretable Structure From Gated RNN
Hou, B., & Zhou, Z. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2967051 · Cited by 108
Riveiro, M., & Thill, S. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103507 · Cited by 93
Explainable Deep Learning for Personalized Age Prediction With Brain Morphology
Lombardi, A., Diacono, D., Amoroso, N., Monaco, A., Tavares, J. M. R. S., Bellotti, R., & Tangaro, S. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.674055 · Cited by 85
Explainable AI, But Explainable to Whom? An Exploratory Case Study of xAI in Healthcare
Gerlings, J., Jensen, M. S., & Shollo, A. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-83620-7_7 · Cited by 95
Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization
Fan, M., Yang, X., Yu, T., Liao, Q. V., & Zhao, J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512943 · Cited by 81
Explainable AI: A review of applications to neuroimaging data
Farahani, F. V., Fiok, K., Lahijanian, B., Karwowski, W., & Douglas, P. K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.906290 · Cited by 87
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIPreprint
Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07487 · Cited by 81
Self-citationdalex: Responsible Machine Learning with Interactive Explainability and Fairness in PythonPreprint
Baniecki, H., Kretowicz, W., Piątyszek, P., Wisniewski, J., & Biecek, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14406 · Cited by 105
Fok, R., & Weld, D. S. (2024) · AI Magazine · doi:10.1002/aaai.12182 · Cited by 81
GLUCOSE: GeneraLized and COntextualized Story Explanations
Mostafazadeh, N., Kalyanpur, A. A., Moon, L., Buchanan, D., Berkowitz, L., Biran, O., & Chu‐Carroll, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.370 · Cited by 98
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
Páez, A. (2019) · PhilPapers (PhilPapers Foundation) · Cited by 80
Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Lió, P., Maggini, M., & Melacci, S. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103822 · Cited by 109
Transparency as design publicity: explaining and justifying inscrutable algorithms
Loi, M., Ferrario, A., & Viganò, E. (2021) · Ethics and Information Technology · doi:10.1007/s10676-020-09564-w · Cited by 79
A Survey of Explainable AI Terminology
Clinciu, M., & Hastie, H. (2019) · doi:10.18653/v1/w19-8403 · Cited by 88
A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning
Sperrle, F., El‐Assady, M., Guo, G., Borgo, R., Chau, D. H., Endert, A., & Keim, D. A. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14329 · Cited by 93
Provenance documentation to enable explainable and trustworthy AI: A literature review
Kale, A., Nguyen, T., Harris, F. C., Li, C., Zhang, J., & Ma, X. (2022) · Data Intelligence · doi:10.1162/dint_a_00119 · Cited by 96
Recommender systems under European AI regulations
Noia, T. D., Tintarev, N., Fatourou, P., & Schedl, M. (2022) · Communications of the ACM · doi:10.1145/3512728 · Cited by 80
Naegelin, M., Weibel, R. P., Kerr, J. I., Schinazi, V. R., Marca, R. L., von Wangenheim, F., Hölscher, C., & Ferrario, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104299 · Cited by 83
Three Levels of AI Transparency
Haresamudram, K., Larsson, S., & Heintz, F. (2023) · Computer · doi:10.1109/mc.2022.3213181 · Cited by 79
Artificial intelligence in the information ecosystem: Affordances for everyday information seeking
Hirvonen, N., Jylhä, V., Lao, Y., & Larsson, S. (2024) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24860 · Cited by 107
Building machines that learn and think with people
Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi‐Xuan, T., Ho, M. K., Mansinghka, V. K., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01991-9 · Cited by 125
How do we assess the trustworthiness of AI? Introducing the trustworthiness assessment model (TrAM)
Schlicker, N., Baum, K. L., Uhde, A., Sterz, S., Hirsch, M. C., & Langer, M. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108671 · Cited by 106
Plan Explanations as Model Reconciliation -- An Empirical Study
Chakraborti, T., Sreedharan, S., Grover, S., & Subbarao Kambhampati, B. (2019) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2019.8673193 · Cited by 79
Mehdiyev, N., & Fettke, P. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-64949-4_1 · Cited by 78
Viganò, L., & Magazzeni, D. (2020) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw51379.2020.00045 · Cited by 96
Interpretable confidence measures for decision support systems
van der Waa, J., Schoonderwoerd, T., van Diggelen, J., & Neerincx, M. A. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102493 · Cited by 78
Is there a role for statistics in artificial intelligence?
Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F., Ickstadt, K., Kestler, H. A., Lederer, J., Leitgöb, H., et al. (2021) · Advances in Data Analysis and Classification · doi:10.1007/s11634-021-00455-6 · Cited by 99
Explainability in music recommender systems
Afchar, D., Melchiorre, A. B., Schedl, M., Hennequin, R., Epure, E. V., & Moussallam, M. (2022) · AI Magazine · doi:10.1002/aaai.12056 · Cited by 100
Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3178382 · Cited by 92
Panagoulias, D. P., Virvou, M. K., & Tsihrintzis, G. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123375 · Cited by 86
Feldman-Maggor, Y., Cukurova, M., Kent, C., & Alexandron, G. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00486-6 · Cited by 97
Explaining Anomalies Detected by Autoencoders Using SHAPPreprint
Antwarg, L., Miller, R. M., Shapira, B., & Rokach, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02407 · Cited by 83
Smith, C. E., Yu, B., Srivastava, A., Halfaker, A., Terveen, L., & Zhu, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376783 · Cited by 77
Why an Android App Is Classified as Malware
Wu, B., Chen, S., Gao, C., Fan, L., Liu, Y., Wen, W., & Lyu, M. R.-T. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3423096 · Cited by 77
Effects of Explanations in AI-Assisted Decision Making: Principles and Comparisons
Wang, X., & Yin, M. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3519266 · Cited by 74
Salvi, M., Seoni, S., Campagner, A., Gertych, A., Acharya, U. R., Molinari, F., & Cabitza, F. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105846 · Cited by 89
Psychological foundations of explainability and interpretability in artificial intelligence
Broniatowski, D. A. (2021) · doi:10.6028/nist.ir.8367 · Cited by 76
The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective
Franklin, G., Stephens, R., Piracha, M., Tiosano, S., Lehouillier, F., Koppel, R., & Elkin, P. (2024) · Life · doi:10.3390/life14060652 · Cited by 76
Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach
Kim, E.-J. (2021) · Journal of Advanced Transportation · doi:10.1155/2021/6685004 · Cited by 76
Černevičienė, J., & Kabašinskas, A. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.827584 · Cited by 84
Explainable multimodal trajectory prediction using attention models
Zhang, K., & Li, L. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103829 · Cited by 81
Generating Counterfactual and Contrastive Explanations using SHAPPreprint
Rathi, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09293 · Cited by 76
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Karimi, A.-H., Schölkopf, B., & Valera, I. (2020) · arXiv · doi:10.48550/arxiv.2002.06278 · Cited by 76
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
