The DOI matches its Crossref record; 114 of the 116 references it cites were found in a registry or an index.
Manipulating and Measuring Model Interpretability
Open accessAbstract
With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed, there have been relatively few experimental studies investigating whether these models achieve their intended effects, such as making people more closely follow a model’s predictions when it is beneficial for them to do so or enabling them to detect when a model has made a mistake. We present a sequence of pre-registered experiments (N = 3, 800) in which we showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box). Predictably, participants who saw a clear model with few features could better simulate the model’s predictions. However, we did not find that participants more closely followed its predictions. Furthermore, showing participants a clear model meant that they were less able to detect and correct for the model’s sizable mistakes, seemingly due to information overload. These counterintuitive findings emphasize the importance of testing over intuition when developing interpretable models.
Overview
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
“Some researchers measure subjective trust using questionnaires, while others measure objective trust related behavior such as reliance.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Poursabzi-Sangdeh et al., 2021
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
“While WOA has been used in the past by researchers in XAI as an alternative trust measurement (Logg et al., 2019; Mucha et al., 2021; Poursabzi-Sangdeh et al., 2021), it has never been explicitly referred to as reliance and thus clearly differentiated from trust to the best of our knowledge.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Poursabzi-Sangdeh et al., 2021
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
“However, across many domains, empirical studies of explanations have produced mixed results [4,11,49,53,58,68,82,87], with some suggesting that explanations increase decisionmakers' tendency to rely on AI predictions even when the AI system is wrong [4,68,82,87]-a phenomenon often referred to as overreliance [10,78].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [68]
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
“On the contrary, several studies found that showing feature-based explanations increases people's tendency to over-rely on the model when it is wrong, compared to showing only the AI predictions [4,68,82,87].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [68]
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
“Such explanations often do not match the end-users' mental models [56], making the explanations ineective [2,40,65] and at times even harmful [13,28,30,47,51].”
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 [40]
Experimental — accuracy not yet measured
The cited source says what the claim attributes to it.
In the citing paper
“Work in the human–computer interaction community has identified that increased transparency can hamper users’ ability to detect sizable model errors and correct for them, “seemingly due to information overload””
From The false hope of current approaches to explainable artificial intelligence in health care (2021) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“and evidence suggests models that use explainability techniques can hamper people's ability to detect when a model makes serious mistakes”
From The false hope of current approaches to explainable artificial intelligence in health care (2021) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“However, many empirical studies on human-AI decision-making did not observe complementary performance [6,16,37,52,58,61,68,82,84,87].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [68]
The cited source says what the claim attributes to it.
In the citing paper
“naive application of existing XAI methods may be ineective [2,40,65] to broader audience of end-users, if not harmful [28,47,49,51].”
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 [40]
The cited source says what the claim attributes to it.
In the citing paper
“and that providing too much detail may reduce user performance due to information overload [44].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [44]
The cited source says what the claim attributes to it.
In the citing paper
“a study by Poursabzi-Sangdeh et al. [48] found that more 'clear' models can actually lead to information overload and hinder people's ability to detect AI errors.”
From Improving Human-AI Collaboration With Descriptions of AI Behavior (2023) · cites this paper as Poursabzi-Sangdeh et al. [48]
The cited source says what the claim attributes to it.
In the citing paper
“However, they choose to use the task of real estate valuation, and how it relates to a decision and an outcome of interest and the utility of explanations in achieving that goal was not established.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Poursabzi-Sangdeh et al. (2021)
The cited source says what the claim attributes to it.
In the citing paper
“Algorithmic transparency similarly reduces people's ability to detect and correct model errors”
From The flaws of policies requiring human oversight of government algorithms (2022) · cites this paper as Poursabzi-Sangdeh et al., 2021
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The complexity of tasks plays a signifcant role in determining the degree of reliance on AI advice, consistent with the fndings of [9, 105].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [105]
Experimental — accuracy not yet measuredIn the citing paper
“Many studies have found that cognitive biases (particularly anchoring bias and automation bias more broadly) can lead users to misunderstand model behavior and place unwarranted trust in AI systems [20, 34, 52, 55].”
From Interactive Model Cards: A Human-Centered Approach to Model Documentation (2022) · cites this paper as [55]
In the citing paper
“Previous works show that the availability of AI's explanations, regardless of their correctness, is likely to increase people's trust in AI [6,54,81,100].”
From Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making (2023) · cites this paper as [81]
In the citing paper
“Many studies have investigated the role of AI in assisting humans in making individual decisions [9,16,36, 37, 50, 51, 55, 58, 69, 83, 84, 89], e.g., predicting whether a person will recidivate in the near future.”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [69]
In the citing paper
“However, recent research has shown that the recommendation-driven explainable/interpretable AI has little effect on decision making [1, 6-8, 28, 32], although this is not always the case [22,23,41].”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [32]
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
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
"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
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691610393980 · Cited by 9,996
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Research works printed but not linked: 10
- Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica (2016).
- 10.2139/ssrn.3465622 · DOI · Paper page
- Philip Johnson-Laird. 1983. Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge University Press.
- Jongbin Jung, Connor Concannon, Ravi Shroff, Sharad Goel, and Daniel G Goldstein. 2020. Simple rules to guide expert classifications. Journal of the Royal Statistical Society: Series A (Statistics in Society) (2020).
- 10.5555/3305381.3305576 · DOI
- Zachary C Lipton. 2016. The mythos of model interpretability. arXiv preprint arXiv:1606.03490(2016). · arXiv
- Jennifer M. Logg. 2017. Theory of Machine: When Do People Rely on Algorithms? (2017). Harvard Business School NOM Unit Working Paper No. 17-086.
- 10.18574/nyu/9781479833641.001.0001 · DOI
- Don A. Norman. 1987. Some Observations on Mental Models. In Human-Computer Interaction: A Multidisciplinary Approach, R. M. Baecker and W. A. S. Buxton (Eds.). Morgan Kaufmann Publishers Inc., 241–244.
- Christian Rudder. 2014. Dataclysm: Love, Sex, Race, and Identity–What Our Online Lives Tell Us about Our Offline Selves. Crown.
Cited by
Sorted by citations
Items 201–300 of 718
The Role of Human Knowledge in Explainable AI
Tocchetti, A., & Brambilla, M. (2022) · Data · doi:10.3390/data7070093 · Cited by 46
feminist data ethics of care for machine learning: The what, why, who and how
Gray, J. E., & Witt, A. (2021) · First Monday · doi:10.5210/fm.v26i12.11833 · Cited by 42
Impact of Model Interpretability and Outcome Feedback on Trust in AI
Ahn, D., Almaatouq, A., Gulabani, M., & Hosanagar, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642780 · Cited by 46
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
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
Towards Quantification of Explainability in Explainable Artificial Intelligence MethodsPreprint
Islam, S. R., Eberle, W., & Ghafoor, S. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1911.10104 · Cited by 35
Rismani, S., Shelby, R. M., Smart, A., Jatho, E. W., Kroll, J. A., Moon, A., & Rostamzadeh, N. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581407 · Cited by 38
Kumar, R., Kumar, M., Tiwari, A., Majid, S. I., Bhadwal, S., Sahu, N., & Avtar, R. (2023) · Water · doi:10.3390/w15223918 · Cited by 34
Effects of explainable artificial intelligence in neurology decision support
Gombolay, G. Y., Silva, A., Schrum, M., Gopalan, N., Hallman‐Cooper, J., Dutt, M., & Gombolay, M. (2024) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.52036 · Cited by 39
Spatola, N. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100099 · Cited by 55
Uncertainty in current and future health wearables
Knowles, B., Smith, A., Poursabzi-Sangdeh, F., Lu, D., & Alabi, H. I. (2018) · Communications of the ACM · doi:10.1145/3199201 · Cited by 34
Self-citationInvestigations of Performance and Bias in Human-AI Teamwork in Hiring
Peng, A., Nushi, B., Kıcıman, E., Inkpen, K. M., & Kamar, E. S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21468 · Cited by 51
Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction
Chu, E., Roy, D., & Andreas, J. (2020) · arXiv · doi:10.48550/arxiv.2007.12248 · Cited by 34
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
Donahue, K., Chouldechova, A., & Kenthapadi, K. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533221 · Cited by 33
Uncertainty in XAI: Human Perception and Modeling Approaches
Chiaburu, T., Haußer, F., & Bießmann, F. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6020055 · Cited by 43
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
On parameter interpretability of phenomenological-based semiphysical models in biology
Lema-Pérez, L., Muñoz‐Tamayo, R., Garcia‐Tirado, J., & Álvarez, H. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.02.002 · Cited by 39
Considerations when learning additive explanations for black-box models
Tan, S., Hooker, G., Koch, P., Gordo, A., & Caruana, R. (2023) · Machine Learning · doi:10.1007/s10994-023-06335-8 · Cited by 88
Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making
Schemmer, M., Hemmer, P., Kühl, N., Benz, C., & Satzger, G. (2022) · arXiv · doi:10.48550/arxiv.2204.06916 · Cited by 33
“Similarly, Poursabzi-Sangdeh et al. [23, p. 1] point out the idea of AR in the form of “making people more closely follow a model’s predictions when it is beneficial for them to do so or enabling them to detect when a model has made a mistake”.”
BackgroundInsights into security and privacy issues in smart healthcare systems based on medical images
Yan, F., Li, N., Iliyasu, A. M., Salama, A. S., & Hirota, K. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103621 · Cited by 40
Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector
Fischer-Abaigar, U., Kern, C. A., Barda, N., & Kreuter, F. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101976 · Cited by 64
Oblizanov, A., Shevskaya, N. V., Kazak, A., Rudenko, М., & Dorofeeva, A. A. (2023) · Applied System Innovation · doi:10.3390/asi6010026 · Cited by 36
The Flaws of Policies Requiring Human Oversight of Government Algorithms
Green, B. (2021) · doi:10.2139/ssrn.3921216 · Cited by 259
Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting
Cabitza, F., Campagner, A., Natali, C., Parimbelli, E., Ronzio, L., & Cameli, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010017 · Cited by 37
Regulating Government AI and the Challenge of Sociotechnical Design
Engstrom, D. F., & Haim, A. (2023) · Annual Review of Law and Social Science · doi:10.1146/annurev-lawsocsci-120522-091626 · Cited by 41
Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making
Sele, D., & Chugunova, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298037 · Cited by 30
Leveraging explainable AI for informed building retrofit decisions: Insights from a survey
Leuthe, D., Mirlach, J., Wenninger, S., & Wiethe, C. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114426 · Cited by 35
Lu, J., Yan, Y., Huang, K., Yin, M., & Zhang, F. (2024) · Group Decision and Negotiation · doi:10.1007/s10726-024-09912-x · Cited by 34
NBDT: Neural-Backed Decision Tree
Wan, A., Dunlap, L., Ho, D. E., Yin, J., Lee, S., Petryk, S., Bargal, S. A., & Gonzalez, J. E. (2021) · Cited by 30
Model learning with personalized interpretability estimation (ML-PIE)
Virgolin, M., Lorenzo, A. D., Randone, F., Medvet, E., & Wahde, M. (2021) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3449726.3463166 · Cited by 36
Does Explainable Artificial Intelligence Improve Human Decision-Making?Preprint
Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y., & Kantarcıoğlu, M. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/d4r9t · Cited by 38
Nourani, M., Roy, C., Block, J. E., Honeycutt, D. R., Rahman, T., Ragan, E. D., & Gogate, V. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3531066 · Cited by 30
Interpretable Question Answering on Knowledge Bases and Text
Sydorova, A., Poerner, N., & Roth, B. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1488 · Cited by 29
Adapting to the algorithm: how accuracy comparisons promote the use of a decision aid
Liang, G., Sloane, J., Donkin, C., & Newell, B. R. (2022) · Cognitive Research Principles and Implications · doi:10.1186/s41235-022-00364-y · Cited by 28
Kim, G., & Lim, S. (2022) · IEEE Access · doi:10.1109/access.2022.3168302 · Cited by 29
Self-fulfilling prophecies and machine learning in resuscitation science
De-Arteaga, M., & Elmer, J. (2023) · Resuscitation · doi:10.1016/j.resuscitation.2022.10.014 · Cited by 28
Autonomy by Design: Preserving Human Autonomy in AI Decision-Support
Buijsman, S., Carter, S. E., & Bermúdez, J. P. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00932-2 · Cited by 37
Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
Suresh, H., Lao, N., & Liccardi, I. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 28
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations
Arora, S., Pruthi, D., Sadeh, N., Cohen, W. W., Lipton, Z. C., & Neubig, G. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i5.20464 · Cited by 41
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making
Guerdan, L., Coston, A., Wu, Z. S., & Holstein, K. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594036 · Cited by 28
Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks
Naiseh, M., Jiang, N., Ma, J., & Ali, R. (2020) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-030-50316-1_13 · Cited by 27
Poly-cam: high resolution class activation map for convolutional neural networks
Englebert, A., Cornu, O., & Vleeschouwer, C. D. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01567-7 · Cited by 31
Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong
Si, C., Goyal, N., Wu, T., Zhao, C., Feng, S., Daumé Iii, H., & Boyd-Graber, J. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.81 · Cited by 26
Modern Smart Cities and Open Research Challenges and Issues of Explainable Artificial Intelligence
Sindiramutty, S. R., Tan, C. E., Tee, W. J., Lau, S. P., Balakrishnan, S., Kaur, S., Jazri, H., & Aslam, M. F. A. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-6361-1.ch015 · Cited by 30
Reicherts, L., Zhang, Z. T., von Oswald, E., Liu, Y., Rogers, Y., & Hassib, M. E. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713295 · Cited by 48
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation
Gómez, Ó., Holter, S., Yuan, J., & Bertini, E. (2021) · Visualization · doi:10.1109/vis49827.2021.9623271 · Cited by 31
Scrutinizing XAI using linear ground-truth data with suppressor variables
Wilming, R., Budding, C., Müller, K., & Haufe, S. (2022) · Machine Learning · doi:10.1007/s10994-022-06167-y · Cited by 30
UnIC: Towards Unmanned Intelligent Cluster and Its Integration into Society
Zhang, F., Yu, J., Lin, D., & Zhang, J. (2022) · Engineering · doi:10.1016/j.eng.2022.02.008 · Cited by 27
ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing
Sun, L., Chan, A., Chang, Y. S., & Dow, S. P. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645159 · Cited by 35
Rebitschek, F. G., Gigerenzer, G., & Wagner, G. G. (2021) · Scientific Reports · doi:10.1038/s41598-021-99802-y · Cited by 28
A United States Fair Lending Perspective on Machine Learning
Hall, P., Cox, B., Dickerson, S., Kannan, A. R., Kulkarni, R., & Schmidt, N. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.695301 · Cited by 32
Fogliato, R., Chouldechova, A., & Lipton, Z. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479572 · Cited by 23
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
Alvarez-Melis, D., Kaur, H., Daumé, H., Wallach, H., & Vaughan, J. W. (2021) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v9i1.18938 · Cited by 27
Self-citationFoundation Models in Healthcare: Opportunities, Risks & Strategies Forward
Thieme, A., Nori, A., Ghassemi, M., Bommasani, R., Andersen, T. O., & Luger, E. (2023) · Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544549.3583177 · Cited by 25
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?Preprint
Zimmermann, R. S., Borowski, J., Geirhos, R., Bethge, M., Wallis, T. S. A., & Brendel, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12447 · Cited by 26
Ebrahimi, S., Abdelhalim, E., Hassanein, K., & Head, M. M. (2024) · European Journal of Information Systems · doi:10.1080/0960085x.2024.2395531 · Cited by 32
Enhancing Intuitive Decision-Making and Reliance Through Human–AI Collaboration: A Review
Xu, G., Murthy, S. V., & Jia, B. (2025) · Informatics · doi:10.3390/informatics12040135 · Cited by 29
How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes
Papenmeier, A., Kern, D., Hienert, D., Kammerer, Y., & Seifert, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501915 · Cited by 29
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)
Speith, T., & Langer, M. (2023) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew57809.2023.00061 · Cited by 25
Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection
Kelodjou, G., Rozé, L., Masson, V., Galárraga, L. A., Gaudel, R., Tchuenté, M., & Termier, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i12.29208 · Cited by 26
Speith, T., Crook, B., Mann, S., Schomäcker, A., & Langer, M. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09769-3 · Cited by 27
Towards a Better Understanding of Evaluating Trustworthiness in AI Systems
Kemmerzell, N., Schreiner, A., Khalid, H., Schalk, M., & Bordoli, L. (2025) · ACM Computing Surveys · doi:10.1145/3721976 · Cited by 34
Regulating the safety of health–related artificial intelligence.
Silva, M., Flood, C., Goldenberg, A., & Singh, D. (2022) · Healthcare Policy | Politiques de Santé · doi:10.12927/hcpol.2022.26824 · Cited by 23
Graham, S. S. (2022) · doi:10.1093/oso/9780197644461.001.0001 · Cited by 25
Hybrid Deep Learning Type-2 Fuzzy Logic Systems For Explainable AI
Chimatapu, R., Hagras, H., Kern, M., & Owusu, G. (2020) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz48607.2020.9177817 · Cited by 23
Kaptchuk, G., Goldstein, D. G., Hargittai, E., Hofman, J. M., & Redmiles, E. M. (2021) · Digital Threats Research and Practice · doi:10.1145/3488307 · Cited by 21
Self-citationMapping the Design Space of Teachable Social Media Feed Experiences
Feng, K. J. K., Koo, X., Tan, L., Bruckman, A. S., McDonald, D. W., & Zhang, A. X. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642120 · Cited by 34
Xu, K., & Shi, J. J. (2024) · Communication Theory · doi:10.1093/ct/qtae016 · Cited by 29
Pierrard, R., Poli, J.-P., & Hudelot, C. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103434 · Cited by 21
Spotting When Algorithms Are Wrong
Buijsman, S., & Veluwenkamp, H. (2022) · Minds and Machines · doi:10.1007/s11023-022-09591-0 · Cited by 25
Nadizar, G., Rovito, L., Lorenzo, A. D., Medvet, E., & Virgolin, M. (2024) · ACM Transactions on Evolutionary Learning and Optimization · doi:10.1145/3643688 · Cited by 23
Reassuring, Misleading, Debunking: Comparing Effects of XAI Methods on Human Decisions
Humer, C., Hinterreiter, A., Leichtmann, B., Mara, M., & Streit, M. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3665647 · Cited by 27
Suresh, H., Lewis, K. M., Guttag, J. V., & Satyanarayan, A. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511160 · Cited by 26
An explanation space to align user studies with the technical development of Explainable AI
Cabour, G., Morales‐Forero, A., Ledoux, É., & Bassetto, S. (2022) · AI & Society · doi:10.1007/s00146-022-01536-6 · Cited by 35
Hegselmann, S., Ertmer, C., Volkert, T., Gottschalk, A., Dugas, M., & Varghese, J. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.960296 · Cited by 27
Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening
Cheng, L., & Chouldechova, A. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555101 · Cited by 23
Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool
Chen, X., Wu, C.-S., Murakhovs’ka, L., Laban, P., Niu, T., Liu, W., & Xiong, C. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3609331 · Cited by 35
Larasati, R. A., Liddo, A. D., & Motta, E. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3631614 · Cited by 24
To Search or To Gen? Exploring the Synergy between Generative AI and Web Search in Programming
Yen, R., Sultanum, N., & Zhao, J. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650867 · Cited by 26
Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening
Cheng, L., & Chouldechova, A. (2022) · arXiv · doi:10.48550/arxiv.2204.05478 · Cited by 20
Incremental XAI: Memorable Understanding of AI with Incremental Explanations
Bo, J. Y., Hao, P., & Lim, B. Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642689 · Cited by 28
Exploring a Behavioral Model of “Positive Friction” in Human-AI Interaction
Chen, Z., & Schmidt, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61353-1_1 · Cited by 24
To Trust or Distrust AI: A Questionnaire Validation Study
Scharowski, N., Perrig, S. A. C., von Felten, N., Aeschbach, L. F., Opwis, K., Wintersberger, P., & Brühlmann, F. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732025 · Cited by 25
Metrics for evaluating the performance of complex engineering system health monitoring models
Lewis, A. D., & Groth, K. M. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108473 · Cited by 20
Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models
Verma, S., Hines, K., & Dickerson, J. P. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20828 · Cited by 29
Kuhl, U., Artelt, A., & Hammer, B. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1087929 · Cited by 20
RetroLens: A Human-AI Collaborative System for Multi-step Retrosynthetic Route Planning
Shi, C., Hu, Y., Wang, S., Ma, S., Zheng, C., Ma, X., & Luo, Q. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581469 · Cited by 25
Gaba, A., Kaufman, Z., Cheung, J., Shvakel, M., Hall, K. W., Brun, Y., & Xiong, C. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3327192 · Cited by 26
Okolo, C. T., Agarwal, D., Dell, N. L., & Vashistha, A. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637348 · Cited by 28
SIDU: Similarity Difference And Uniqueness Method for Explainable AI
Muddamsetty, S. M., Jahromi, M. N. S., & Moeslund, T. B. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190952 · Cited by 20
ViCE: Visual Counterfactual Explanations for Machine Learning ModelsPreprint
Gómez, Ó., Holter, S., Yuan, J., & Bertini, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.02428 · Cited by 20
Gambo, I., Massenon, R., Lin, C., Ogundokun, R. O., Agarwal, S., & Pak, W. (2024) · IEEE Access · doi:10.1109/access.2024.3443527 · Cited by 21
Rule extraction with guarantees from regression models
Johansson, U., Sönströd, C., Löfström, T., & Boström, H. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108554 · Cited by 19
Technology paternalism and smart products: Review, synthesis, and research agenda
Rochi, M. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122557 · Cited by 21
Multi-objective Feature Attribution Explanation For Explainable Machine Learning
Wang, Z., Huang, C., Li, Y., & Yao, X. (2023) · ACM Transactions on Evolutionary Learning and Optimization · doi:10.1145/3617380 · Cited by 21
Measuring the effect of mental workload and explanations on appropriate AI reliance using EEG
Zhang, Z. T., Argın, S. K., Bilen, M. B., Urgun, D., Deniz, S. M., Liu, Y., & Hassib, M. E. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2431055 · Cited by 22
Bridging Generations using AI-Supported Co-Creative Activities
Kim, C. Y., Sato, A. J., White, N. T., Ho, H.-R., Lee, C. P., Hwang, Y., & Mutlu, B. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713718 · Cited by 26
Between transparency and trust: identifying key factors in AI system perception
Cetinkaya, N. E., & Krämer, N. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2533358 · Cited by 26
Véran, T., Portier, P.-É., & Fouquet, F. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105534 · Cited by 18
Socially intelligent machines that learn from humans and help humans learn
Gweon, H., Fan, J. E., & Kim, B. (2023) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2022.0048 · Cited by 24
Metadata
- Authors
- Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H.
- Affiliations
- Microsoft (United States) (US)
- Microsoft Research, United States
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-52
- OpenAlex
- W2788362053
- Semantic Scholar
- 2f4e42ab1368b83e38e3011fcec4edb57267b5d2
- Cited by
- 728
- References
- 116
- Citations by year
- 2026: 94 · 2025: 97 · 2024: 118 · 2023: 106 · 2022: 111 · 2021: 90
- FWCI
- 48.67
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- model interpretability, explainable AI, black-box models, model transparency, feature count, error detection, information overload, trust in machine learning, pre-registered experiment
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
