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 801–900 of 1,050
Cystoscopy Accuracy in Detecting Bladder Tumors: A Prospective Video-Confirmed Study
Guldhammer, C. S., Vásquez, J. L., Kristensen, V. M., Norus, T. P., Nadler, N., Jensen, J. B., & Azawi, N. (2023) · Cancers · doi:10.3390/cancers16010160 · Cited by 43
Alsamhi, S. H., Kumar, S., Hawbani, A., Shvetsov, A. V., Zhao, L., & Guizani, M. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10271-7 · Cited by 44
The Rise of the AI Co-Pilot: Lessons for Design from Aviation and Beyond
Sellen, A. J., & Horvitz, E. (2024) · Communications of the ACM · doi:10.1145/3637865 · Cited by 43
Machine learning driven bond performance prediction between FRP bars and coral aggregate concrete
Tang, Y., Zhou, C., Wang, W., Zhang, W., & Cheng, Y. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.137684 · Cited by 45
You, X., Huang, Y., Zhang, C. L., Wang, J., Yin, H., & Wu, H. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4257-6 · Cited by 43
Explanations by arbitrated argumentative dispute
Čyras, K., Birch, D., Guo, Y., Toni, F., Dulay, R., Turvey, S., Greenberg, D., & Hapuarachchi, T. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.012 · Cited by 43
Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI
Nicodème, C. (2020) · Conference on Human System Interactions · doi:10.1109/hsi49210.2020.9142668 · Cited by 43
Machine Thinking, Fast and Slow
Bonnefon, J., & Rahwan, I. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.09.007 · Cited by 47
Hoque, M. N., & Mueller, K. D. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3102051 · Cited by 51
Realizing AI in Healthcare: Challenges Appearing in the Wild
Andersen, T. O., Nunes, F., Wilcox, L. G., Kaziunas, E., Matthiesen, S., & Magrabi, F. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3441347 · Cited by 43
Is explainable artificial intelligence intrinsically valuable?
Colaner, N. R. (2021) · AI & Society · doi:10.1007/s00146-021-01184-2 · Cited by 43
Personalising Explainable Recommendations: Literature and Conceptualisation
Naiseh, M., Jiang, N., Ma, J., & Ali, R. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-45691-7_49 · Cited by 42
SAT-Based Rigorous Explanations for Decision Lists
Ignatiev, A., & Marques-Silva, J. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80223-3_18 · Cited by 42
Fuzzy Rule-Based Local Surrogate Models for Black-Box Model Explanation
Zhu, X., Wang, D., Pedrycz, W., & Li, Z. (2022) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2022.3218426 · Cited by 42
Effects of AI and Logic-Style Explanations on Users’ Decisions Under Different Levels of Uncertainty
Cau, F. M., Hauptmann, H., Spano, L. D., & Tintarev, N. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3588320 · Cited by 44
Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)
Crook, B., Schlüter, M., & Speith, T. (2023) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew57809.2023.00060 · Cited by 47
An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis
Bellantuono, L., Tommasi, R., Pantaleo, E., Verri, M., Amoroso, N., Crucitti, P., Gioacchino, M. D., Longo, F., Monaco, A., Naciu, A. M., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-43856-7 · Cited by 49
Comes, T. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09750-0 · Cited by 55
Miró-Nicolau, M., Jaume-i-Capó, A., & Moyà-Alcover, G. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104179 · Cited by 55
The Fairness of Credit Scoring Models
Christophe Hurlin, Y., Pérignon, C., & Saurin, S. (2021) · Management Science · doi:10.1287/mnsc.2022.03888 · Cited by 88
AI Alignment: A Contemporary Survey
Ji, J., Qiu, T., Chen, B., Zhou, J., Zhang, B., Hong, D., Lou, H., Wang, K., Duan, Y., He, Z., et al. (2025) · ACM Computing Surveys · doi:10.1145/3770749 · Cited by 772
Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation
Sreedharan, S., Chakraborti, T., & Subbarao Kambhampati, B. (2018) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v28i1.13930 · Cited by 42
The grammar of interactive explanatory model analysis
Baniecki, H., Parzych, D., & Biecek, P. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00924-w · Cited by 50
Directive Explanations for Actionable Explainability in Machine Learning Applications
Singh, R., Miller, T., Lyons, H., Sonenberg, L., Velloso, E., Vetere, F., Howe, P. D. L., & Dourish, P. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3579363 · Cited by 57
Self-citationHamon, R., Junklewitz, H., Malgieri, G., Hert, P. D., Beslay, L., & Sanchez, I. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445917 · Cited by 42
Data Science for Economics and Finance
Consoli, S., Recupero, D. R., & Saisana, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-66891-4 · Cited by 53
Theodorou, L., Massiceti, D., Zintgraf, L., Stumpf, S., Morrison, C., Cutrell, E., Harris, M. T., & Hofmann, K. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471225 · Cited by 42
Chiou, E. K., Demir, M., Buchanan, V., Corral, C. C., Endsley, M. R., Lematta, G. J., Cooke, N. J., & McNeese, N. J. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-021-00834-1 · Cited by 42
On Explaining Decision TreesPreprint
Izza, Y., Ignatiev, A., & Marques-Silva, J. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11034 · Cited by 107
Machine Learning Explainability for External Stakeholders
Bhatt, U., Andrus, M., Weller, A., & Xiang, A. (2020) · arXiv · doi:10.48550/arxiv.2007.05408 · Cited by 41
Towards Accountability: Providing Intelligible Explanations in Autonomous Driving
Omeiza, D., Webb, H., Jirotka, M. D., & Kunze, L. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575917 · Cited by 41
The Explanation Game: A Formal Framework for Interpretable Machine Learning
Watson, D., & Floridi, L. (2021) · Philosophical studies series · doi:10.1007/978-3-030-81907-1_11 · Cited by 42
Marketing Strategies 4.0: Recent Trends and Technologies in Marketing
Kaur, R., Singh, R., Gehlot, A., Priyadarshi, N., & Twala, B. (2022) · Sustainability · doi:10.3390/su142416356 · Cited by 76
Prediction of shear strength of RC deep beams based on interpretable machine learning
Ma, C., Wang, S., Zhao, J., Xiao, X., Xie, C., & Feng, X. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.131640 · Cited by 41
Mill, E., Garn, W., Ryman-Tubb, N. F., & Turner, C. J. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01405121 · Cited by 53
Mehdiyev, N., Majlatow, M., & Fettke, P. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-05943-4 · Cited by 41
Explaining Deep Learning Models with Constrained Adversarial Examples
Moore, J., Hammerla, N., & Watkins, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29908-8_4 · Cited by 41
Mutual Explanations for Cooperative Decision Making in Medicine
Schmid, U., & Finzel, B. (2020) · Künstliche Intell. · doi:10.1007/s13218-020-00633-2 · Cited by 42
Thellman, S., & Ziemke, T. (2021) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3461781 · Cited by 45
Brain-Inspired Search Engine Assistant Based on Knowledge Graph
Zhao, X., Chen, H., Xing, Z., & Miao, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3113026 · Cited by 43
Simkute, A., Luger, E. A., Jones, B., Evans, M., & Jones, R. (2021) · Journal of Responsible Technology · doi:10.1016/j.jrt.2021.100017 · Cited by 46
An assessment framework for explainable AI with applications to cybersecurity
Calzarossa, M. C., Giudici, P., & Zieni, R. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11141-w · Cited by 40
Evaluating the Interpretability of Generative Models by Interactive Reconstruction
Ross, A., Chen, N., Hang, E. Z., Glassman, E. L., & Doshi-Velez, F. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445296 · Cited by 40
A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences
Bircan, T., & Salah, A. A. A. (2022) · Mathematics · doi:10.3390/math10234398 · Cited by 49
Leichtmann, B., Hinterreiter, A., Humer, C., Streit, M., & Mara, M. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2221605 · Cited by 47
Explainable Interactive Evolutionary Multiobjective Optimization
Corrente, S., Greco, S., Benedetto Matarazzo, A., & Słowiński, R. (2021) · Omega · doi:10.1016/j.omega.2023.102925 · Cited by 40
C-XAI: A conceptual framework for designing XAI tools that support trust calibration
Naiseh, M., Simkute, A., Zieni, B., Jiang, N., & Ali, R. (2024) · Journal of Responsible Technology · doi:10.1016/j.jrt.2024.100076 · Cited by 46
Ethics and artificial intelligence
Galiana, L. I., Corral-Gudino, L., & Miramontes‐González, J. P. (2024) · Revista Clínica Española (English Edition) · doi:10.1016/j.rceng.2024.02.003 · Cited by 53
Gupta, S., Kumar, A., & Maiti, J. (2024) · Safety Science · doi:10.1016/j.ssci.2024.106590 · Cited by 48
From human-centered to symbiotic artificial intelligence: a focus on medical applications
Desolda, G., Esposito, A., Lanzilotti, R., Piccinno, A., & Costabile, M. F. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20414-5 · Cited by 62
Mir, A. N., Rizvi, D. R., & Ahmad, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110519 · Cited by 44
Aderemi, I. A., Kehinde, T. O., Okwor, U. D., Hussain, A. K., Adjei, K. Y., & Ekechi, C. C. (2025) · IEEE Sensors Reviews · doi:10.1109/sr.2025.3595500 · Cited by 40
Dark Patterns of Explainability, Transparency, and User Control for Intelligent Systems.
Chromik, M., Eiband, M., Völkel, S. T., & Buschek, D. (2019) · IUI Workshops · Cited by 42
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Gao, X.-F., Gong, R., Zhao, Y.-Z., Wang, S., Shu, T., & Zhu, S.-C. (2020) · IEEE International Symposium on Robot and Human Interactive Communication · doi:10.1109/ro-man47096.2020.9223595 · Cited by 48
Hase, P., & Bansal, M. (2022) · LNLS · doi:10.18653/v1/2022.lnls-1.4 · Cited by 71
ARM-Net: Adaptive Relation Modeling Network for Structured Data
Cai, S., Zheng, K., Chen, G., Jagadish, H. V., Ooi, B. C., & Zhang, M. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457321 · Cited by 43
Stieler, F., Rabe, F., & Bauer, B. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00199 · Cited by 42
Principles of Explainable Artificial Intelligence
Guidotti, R., Monreale, A., Pedreschi, D., & Giannotti, F. (2021) · Springer eBooks · doi:10.1007/978-3-030-76409-8_2 · Cited by 47
Solanke, A. A. (2022) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2022.301403 · Cited by 52
Adding interpretability to predictive maintenance by machine learning on sensor data
Steurtewagen, B., & den Poel, D. V. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107381 · Cited by 39
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
Yang, L., Li, J., Cunningham, P., Zhang, Y., Smyth, B., & Dong, R. (2021) · doi:10.18653/v1/2021.acl-long.26 · Cited by 52
Chatti, M. A., Guesmi, M., Vorgerd, L., Ngo, T., Joarder, S., Ain, Q. U., & Muslim, A. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3503252.3531304 · Cited by 42
Explainable software systems: from requirements analysis to system evaluation
Chazette, L., Brunotte, W., & Speith, T. (2022) · Requirements Engineering · doi:10.1007/s00766-022-00393-5 · Cited by 52
Impact of Transparency and Explanations on Trust and Situation Awareness in Human–Robot Teams
Ezenyilimba, A., Wong, M. E., Hehr, A. J., Demir, M., Wolff, A. T., Chiou, E. K., & Cooke, N. J. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221136358 · Cited by 39
Stable and actionable explanations of black-box models through factual and counterfactual rules
Guidotti, R., Monreale, A., Ruggieri, S., Naretto, F., Turini, F., Pedreschi, D., & Giannotti, F. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00878-5 · Cited by 39
Perera, U. A. K. K., Coralage, D. T. S., Ekanayake, I. U., Alawatugoda, J., & Meddage, D. P. P. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.101920 · Cited by 39
When AI Fails, Who Do We Blame? Attributing Responsibility in Human–AI Interactions
Schoenherr, J. R., & Thomson, R. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3370095 · Cited by 39
Hou, T.-Y., Tseng, Y.-C., & Yuan, C. W. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102775 · Cited by 43
Towards a Theory of Explanations for Human–Robot Collaboration
Sridharan, M., & Meadows, B. (2019) · KI - Künstliche Intelligenz · doi:10.1007/s13218-019-00616-y · Cited by 63
Eifler, R., Cashmore, M., Hoffmann, J., Magazzeni, D., & Steinmetz, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i06.6534 · Cited by 39
Ma, B., Nie, S., Ji, M., Song, J., & Wang, W. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/8879616 · Cited by 39
Understandable and trustworthy explainable robots: A sensemaking perspective
Papagni, G., & Koeszegi, S. T. (2020) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2021-0002 · Cited by 49
Increasing the User Experience in Autonomous Driving through different Feedback Modalities
Schneider, T., Ghellal, S., Love, S., & Gerlicher, A. R. S. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450687 · Cited by 39
Valente, F., Henriques, J. M. O., Paredes, S., Rocha, T., Carvalho, P., & Morais, J. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102113 · Cited by 43
Towards Design Principles for User-Centric Explainable AI in Fraud Detection
Cirqueira, D., Helfert, M., & Bezbradica, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77772-2_2 · Cited by 43
Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation
Ladkat, A. S., Bangare, S. L., Jagota, V., Sanober, S., Beram, S. M., Rane, K. P., & Singh, B. K. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4271711 · Cited by 49
Beyond Explaining Single Item Recommendations
Tintarev, N., & Masthoff, J. (2012) · Springer eBooks · doi:10.1007/978-1-0716-2197-4_19 · Cited by 42
Principles of Explanation in Human-AI Systems
Mueller, S. T., Veinott, E. S., Hoffman, R. R., Klein, G., Alam, L., Mamun, T., & Clancey, W. J. (2021) · arXiv · doi:10.48550/arxiv.2102.04972
Local Explanations Via Necessity and Sufficiency: Unifying Theory and PracticePreprint
Watson, D., Gultchin, L., Taly, A., & Floridi, L. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3825636 · Cited by 40
Alfeo, A. L., Zippo, A. G., Catrambone, V., Cimino, M. G. C. A., Toschi, N., & Valenza, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107550 · Cited by 38
Regional Tree Regularization for Interpretability in Deep Neural Networks
Wu, M., Parbhoo, S., Hughes, M., Kindle, R., Celi, L., Zazzi, M., Roth, V., & Doshi-Velez, F. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6112 · Cited by 38
Relation-Based Counterfactual Explanations for Bayesian Network Classifiers
Albini, E., Rago, A., Baroni, P., & Toni, F. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/63 · Cited by 39
Stange, S., Hassan, T., Schröder, F., Konkol, J., & Kopp, S. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.866920 · Cited by 43
Why Did I Fail? A Causal-Based Method to Find Explanations for Robot Failures
Diehl, M., & Ramírez-Amaro, K. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3188889 · Cited by 38
Celar, L., & Byrne, R. M. J. (2023) · Memory & Cognition · doi:10.3758/s13421-023-01407-5 · Cited by 45
Sturm, T., Pumplun, L., Gerlach, J. P., Kowalczyk, M., & Buxmann, P. (2023) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2023.101790 · Cited by 48
Lee, K. S., Lee, J.-J., Aucremanne, C., Shah, I., & Ghahramani, A. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110771 · Cited by 41
A historical perspective of biomedical explainable AI research
Malinverno, L., Barros, V., Ghisoni, F., Visonà, G., Kern, R., Nickel, P. J., Ventura, B. E., Šimić, I., Stryeck, S., Manni, F., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100830 · Cited by 46
Kaufman, R. P., Costa, J. M. D. R., & Kimani, E. (2024) · Scientific Reports · doi:10.1038/s41598-024-62052-9 · Cited by 44
Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review
Bach, T. A., Kristiansen, J. K., Babić, A., & Jacovi, A. (2024) · IEEE Access · doi:10.1109/access.2024.3437190 · Cited by 46
AI for Explaining Decisions in Multi-Agent Environments
Kraus, S., Azaria, A., Fiosina, J., Greve, M., Hazon, N., Kolbe, L. M., Lembcke, T.-B., Muller, J. P., Schleibaum, S., & Vollrath, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i09.7077 · Cited by 41
Multi-layered explanations from algorithmic impact assessments in the GDPR
Kaminski, M. E., & Malgieri, G. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372875 · Cited by 40
User preferences for privacy features in digital assistants
Ebbers, F., Zibuschka, J., Zimmermann, C., & Hinz, O. F. (2020) · Electronic Markets · doi:10.1007/s12525-020-00447-y · Cited by 46
Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds
Alcantara, R. S., Day, E. M., Hahn, M. E., & Grabowski, A. M. (2021) · PeerJ · doi:10.7717/peerj.11199 · Cited by 41
Design of a Machine Learning-Based Self-driving Car
Soni, A. K., Dharmacharya, D., Pal, A., Srivastava, V. K., Shaw, R. N., & Ghosh, A. (2021) · Studies in computational intelligence · doi:10.1007/978-981-16-0598-7_11 · Cited by 38
Papagni, G., & Koeszegi, S. T. (2021) · Minds and Machines · doi:10.1007/s11023-021-09567-6 · Cited by 43
On Efficiently Explaining Graph-Based Classifiers
Huang, X., Izza, Y., Ignatiev, A., & Marques-Silva, J. P. (2021) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2021/34 · Cited by 38
The Robustness of Counterfactual Explanations Over Time
Ferrario, A., & Loi, M. (2022) · IEEE Access · doi:10.1109/access.2022.3196917 · Cited by 47
Brankovic, A., Cook, D., Rahman, J., Delaforce, A., Li, J., Magrabi, F., Cabitza, F., Coiera, E., & Bradford, D. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01764-2 · Cited by 37
A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI.
Chromik, M., & Schuessler, M. (2020) · Cited by 39
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
