The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies
Open accessAbstract
AI systems are adopted in numerous domains due to their increasingly strong predictive performance. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time-consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our work highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other’s work and produce generalizable scientific knowledge. We also hope this work will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.
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
“there is a limited understanding around how the important task characteristics of complexity and uncertainty shape human decision-making and human-AI team performance.”
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 [68]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Both of these have been studied in prior work on AI-assisted decision making [54], as well as specifically for studying the effect of uncertainty expression [102].”
From "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust (2024) · cites this paper as [54]
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
"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
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · cited 1×
“Recently, there are growing interest in providing documentation or an “About Me” page to present such global information e.g., Model cards [78], Fact-Sheets [7].”
BackgroundAnchors: High-Precision Model-Agnostic Explanations
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162 · cited 6×
“The most commonly used metric is forward simulation [1, 2, 15, 21, 24, 36, 48, 80, 81, 83, 87, 105, 111], by asking participants to simulate a model’s predictions on unseen instances. and what design impacts the perception is an active research area.”
BackgroundMethodInfluentialAlgorithm appreciation: People prefer algorithmic to human judgment
Logg, J. M., Minson, J. A., & Moore, D. A. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2018.12.005 · Cited by 2,061
He, S., & Lau, R. W. H. (2021) · Computer Graphics Forum · doi:10.1111/(issn)1467-8659 · Cited by 1,236
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018) · Management Science · doi:10.1287/mnsc.2016.2643 · Cited by 1,230 · cited 9×
“…mturk [1, 9, 11, 15, 21, 23, 24, 26, 29, 30, 32, 35, 38, 41, 43–45, 47, 60–62, 71, 73, 74, 79, 82, 83, 86, 91, 94, 110, 112], university [24, 30, 48, 57, 61, 63, 68, 72, 75, 81, 82, 99, 106, 111], prolific [2, 36], company [8, 38, 56, 73, 75, 108], qualitrics [84], local community [12, 58],…”
BackgroundMethodInfluentialAmazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *
Dastin, J. (2022) · Ethics of Data and Analytics · doi:10.1201/9781003278290-44 · Cited by 1,165
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124
Power to the People: The Role of Humans in Interactive Machine Learning
Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014) · AI Magazine · doi:10.1609/aimag.v35i4.2513 · Cited by 1,084
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044 · cited 1×
“This misalignment is analogous to the discrepancies between the recent burst of COVID-related machine learning papers and clinical practices [88].”
ResultRoberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 902
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
(2024) · doi:10.1145/3613904 · Cited by 896
Consumer credit-risk models via machine-learning algorithms
Khandani, A. E., Kim, A. J., & Lo, A. W. (2010) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2010.06.001 · Cited by 864
A survey of human-in-the-loop for machine learning
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.014 · Cited by 800
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. S. (2020) · arXiv · doi:10.48550/arxiv.2006.14779 · Cited by 754
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · cited 8×
“Subjective metrics of understanding typically ask participants to directly rate their understanding of the AI [5, 12, 15, 18, 23, 75, 91, 105, 108], or some variations of it such as confidence in understanding [57], ease of understanding [46, 83], or confidence in simulation [2, 80].”
BackgroundInfluentialInterpretable Decision Sets: A Joint Framework for Description and Prediction
Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687
Frøkjær, E., Hertzum, M., & Hornbæk, K. (2000) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/332040.332455 · Cited by 654
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Hohman, F., Kahng, M., Pienta, R., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2843369 · Cited by 582
Why and why not explanations improve the intelligibility of context-aware intelligent systems
Lim, B. Y., Dey, A. K., & Avrahami, D. (2009) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1519023 · Cited by 552 · cited 6×
“…36, 45, 46, 68, 73, 82, 84, 101, 112], less than equals to $15 [2, 24, 38, 41, 47, 48, 72, 94], more than $15 [5, 9, 11, 15, 43, 44, 50, 57, 70, 72, 99], bonus available [2, 16, 21, 23, 29, 30, 41, 43, 61, 62, 71, 91, 105] Table 3: Types of tasks grouped by field in human-AI collaboration papers.”
BackgroundInfluentialFormalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445923 · Cited by 545
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Yin, M., Wortman Vaughan, J., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300509 · Cited by 527 · cited 7×
“…employees [38, 56], Platform mturk [1, 9, 11, 15, 21, 23, 24, 26, 29, 30, 32, 35, 38, 41, 43–45, 47, 60–62, 71, 73, 74, 79, 82, 83, 86, 91, 94, 110, 112], university [24, 30, 48, 57, 61, 63, 68, 72, 75, 81, 82, 99, 106, 111], prolific [2, 36], company [8, 38, 56, 73, 75, 108], qualitrics…”
BackgroundMethodInfluentialVeale, M., Van Kleek, M., & Binns, R. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174014 · Cited by 490
Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance
Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5285 · Cited by 466
The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems
Bussone, A., Stumpf, S., & O'Sullivan, D. (2015) · 2015 International Conference on Healthcare Informatics · doi:10.1109/ichi.2015.26 · Cited by 441
FactSheets: Increasing trust in AI services through supplier's declarations of conformity
Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942288 · Cited by 434
Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300234 · Cited by 405
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?Preprint
Hase, P., & Bansal, M. (2020) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2005.01831 · Cited by 396
The Principles and Limits of Algorithm-in-the-Loop Decision Making
Green, B., & Chen, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359152 · Cited by 392 · cited 8×
“…$10 [1, 16, 23, 30, 36, 45, 46, 68, 73, 82, 84, 101, 112], less than equals to $15 [2, 24, 38, 41, 47, 48, 72, 94], more than $15 [5, 9, 11, 15, 43, 44, 50, 57, 70, 72, 99], bonus available [2, 16, 21, 23, 29, 30, 41, 43, 61, 62, 71, 91, 105] Table 3: Types of tasks grouped by field in human-AI…”
BackgroundMethodInfluentialToo much, too little, or just right? Ways explanations impact end users' mental models
Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I., & Wong, W.-K. (2013) · 2013 IEEE Symposium on Visual Languages and Human Centric Computing · doi:10.1109/vlhcc.2013.6645235 · Cited by 388
Explanations as Mechanisms for Supporting Algorithmic Transparency
Rader, E., Cotter, K., & Cho, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173677 · Cited by 388
Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377
Self-citationImproving Content-based and Hybrid Music Recommendation using Deep Learning
Wang, X., & Wang, Y. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654940 · Cited by 370
Impact of a deep learning assistant on the histopathologic classification of liver cancer
Kiani, A., Uyumazturk, B., Rajpurkar, P., Wang, A., Gao, R., Jones, E., Yu, Y., Langlotz, C. P., Ball, R. L., Montine, T. J., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0232-8 · Cited by 325 · cited 5×
“Sample size (per condition) less than equals to 10 [17, 67], less than equals to 50 [5, 9, 13, 34, 35, 46, 55, 56, 58, 70, 87], less than equals to 100 [41, 61, 62], less than equals to 1000 [18, 29].”
BackgroundInfluentialWang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320
The Values Encoded in Machine Learning Research
Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., & Bao, M. (2021) · arXiv · doi:10.48550/arxiv.2106.15590 · Cited by 317
Šabanović, S. (2010) · International Journal of Social Robotics · doi:10.1007/s12369-010-0066-7 · Cited by 314
Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems
Buçinca, Z., Lin, P., Gajos, K. Z., & Glassman, E. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377498 · Cited by 307
Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders
Cheng, H.-F., Wang, R., Zhang, Z., O'Connell, F., Gray, T., Harper, F. M., & Zhu, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300789 · Cited by 292
Explaining models: an empirical study of how explanations impact fairness judgment
Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., & Dugan, C. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302310 · Cited by 286
Self-citationUpdates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff
Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33012429 · Cited by 284
Kocielnik, R., Amershi, S., & Bennett, P. N. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300641 · Cited by 277
Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments
Green, B., & Chen, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287563 · Cited by 275
Procedural Justice in Algorithmic Fairness
Lee, M. K., Jain, A., Jin, H., Ojha, S., & Kusbit, D. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359284 · Cited by 267
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Hase, P., & Bansal, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.491 · Cited by 254
Kay, M., Kola, T., Hullman, J. R., & Munson, S. A. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858558 · Cited by 239
How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies
Vereschak, O., Bailly, G., & Caramiaux, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476068 · Cited by 229
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Kulesza, T., Stumpf, S., Burnett, M., & Kwan, I. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207678 · Cited by 225
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
De-Arteaga, M., Fogliato, R., & Chouldechova, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376638 · Cited by 221
The effects of example-based explanations in a machine learning interface
Cai, C. J., Jongejan, J., & Holbrook, J. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302289 · Cited by 220
How do visual explanations foster end users' appropriate trust in machine learning?
Yang, F., Huang, Z., Scholtz, J., & Arendt, D. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377480 · Cited by 200
Jacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification
Nguyen, D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1097 · Cited by 175
Evaluating saliency map explanations for convolutional neural networks: a user study
Alqaraawi, A., Schuessler, M., Weiß, P., Costanza, E., & Berthouze, N. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377519 · Cited by 163
Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems
Kunkel, J., Donkers, T., Michael, L., Barbu, C.-M., & Ziegler, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300717 · Cited by 156
Narayanan, M., Chen, E., He, J., Kim, B., Gershman, S., & Doshi-Velez, F. (2018) · arXiv · doi:10.48550/arxiv.1802.00682 · Cited by 153
I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI
Chromik, M., Eiband, M., Buchner, F., Krüger, A., & Butz, A. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450644 · Cited by 144 · cited 7×
“The most commonly used metric is forward simulation [1, 2, 15, 21, 24, 36, 48, 80, 81, 83, 87, 105, 111], by asking participants to simulate a model’s predictions on unseen instances. and what design impacts the perception is an active research area.”
BackgroundInfluentialA Human-AI Collaborative Approach for Clinical Decision Making on Rehabilitation Assessment
Lee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. B. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445472 · Cited by 137
Visual, textual or hybrid: the effect of user expertise on different explanations
Szymanski, M., Millecamp, M., & Verbert, K. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450662 · Cited by 131
Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks
Lu, Z., & Yin, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445562 · Cited by 131
The limits of human predictions of recidivism
Lin, Z. J., Jung, J., Goel, S., & Skeem, J. L. (2020) · Science Advances · doi:10.1126/sciadv.aaz0652 · Cited by 129
An Evaluation of the Human-Interpretability of Explanation
Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S., & Doshi-Velez, F. (2019) · arXiv · doi:10.48550/arxiv.1902.00006 · Cited by 125 · cited 9×
“The most commonly used metric is accuracy , measured as the percentage of correctly predicted instances (or equivalently, error rate, the percentage of incorrectly predicted instances) [9, 20, 21, 33, 35, 39, 60–63, 75, 76, 79, 80, 86].”
BackgroundMethodInfluential"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Lai, V., Liu, H., & Tan, C. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376873 · Cited by 123
Self-citationMental Models of AI Agents in a Cooperative Game Setting
Gero, K. I., Ashktorab, Z., Dugan, C., Pan, Q., Johnson, J., Geyer, W., Ruiz, M., Miller, S., Millen, D. R., Campbell, M., et al. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376316 · Cited by 121 · cited 3×
“The most common objective metric is time taken on the task [1, 8, 20, 23, 36, 38, 41, 56, 60, 63, 70, 71, 79, 91, 96, 106, 108].”
BackgroundMethodAn empirical study on the perceived fairness of realistic, imperfect machine learning models
Harrison, G., Hanson, J., Jacinto, C., Ramirez, J., & Ur, B. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372831 · Cited by 120
Human Decision Making with Machine Assistance: An Experiment on Bailing and Jailing
Grgić-Hlača, N., Engel, C., & Gummadi, K. P. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359280 · Cited by 113
Smith-Renner, A., Fan, R., Birchfield, M., Wu, T., Boyd-Graber, J., Weld, D. S., & Findlater, L. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376624 · Cited by 112
Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers
Ghai, B., Liao, Q. V., Zhang, Y., Bellamy, R., & Mueller, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432934 · Cited by 106
Self-citationAnik, A. I., & Bunt, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445736 · Cited by 103 · cited 9×
“Subjective metrics of understanding typically ask participants to directly rate their understanding of the AI [5, 12, 15, 18, 23, 75, 91, 105, 108], or some variations of it such as confidence in understanding [57], ease of understanding [46, 83], or confidence in simulation [2, 80].”
BackgroundInfluentialInsurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models
Yang, Y. A., Qian, W., & Zou, H. (2016) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2016.1200981 · Cited by 100
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
Human-Centric Justification of Machine Learning Predictions
Biran, O., & McKeown, K. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/202 · Cited by 84
Effect of Information Presentation on Fairness Perceptions of Machine Learning Predictors
van Berkel, N., Goncalves, J., Russo, D., Hosio, S., & Skov, M. B. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445365 · Cited by 84
Levy, A., Agrawal, M., Satyanarayan, A., & Sontag, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445522 · Cited by 73 · cited 10×
“…directly recruited [70], not mentioned [17, 66, 102] Study design between [1, 2, 11, 15, 17, 18, 21, 23, 29, 30, 32, 36, 38, 41, 43–45, 47, 60–63, 70, 72–75, 79, 81–83, 86, 91, 94, 101, 105, 106, 110, 112], within [9, 35, 45, 46, 66–68, 87, 94, 98, 106, 108], mixed methods [2, 12, 24, 61, 72,…”
BackgroundInfluentialDo I trust my machine teammate?: an investigation from perception to decision
Yu, K., Berkovsky, S., Taib, R., Zhou, J., & Chen, F. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302277 · Cited by 71
A Slow Algorithm Improves Users' Assessments of the Algorithm's Accuracy
Park, J. S., Barber, R., Kirlik, A., & Karahalios, K. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359204 · Cited by 65
(2024) · CQ Press eBooks · doi:10.4135/9781071920541.n114 · Cited by 64
Feature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity
Carton, S., Mei, Q., & Resnick, P. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7282 · Cited by 58
Lee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415227 · Cited by 57
Visualizing Uncertainty and Alternatives in Event Sequence Predictions
Guo, S., Du, F., Malik, S., Koh, E., Kim, S., Liu, Z., Kim, Y., Hongyuan Zha, B., & Cao, N. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300803 · Cited by 56
Leveraging rationales to improve human task performance
Das, D., & Chernova, S. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377512 · Cited by 52
Assessing the Local Interpretability of Machine Learning ModelsPreprint
Slack, D., Friedler, S. A., Scheidegger, C., & Roy, C. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.03501 · Cited by 50
Lučić, A., Haned, H., & de Rijke, M. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372824 · Cited by 46
A Human-Grounded Evaluation of SHAP for Alert ProcessingPreprint
Weerts, H., van Ipenburg, W., & Pechenizkiy, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03324 · Cited by 45
Taking advice from intelligent systems: the double-edged sword of explanations
Ehrlich, K., Kirk, S. E., Patterson, J., Rasmussen, J. C., Ross, S. I., & Gruen, D. (2011) · International Conference on Intelligent User Interfaces · doi:10.1145/1943403.1943424 · Cited by 42
Advances in Human Factors in Robots and Unmanned Systems
(2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-94346-6 · Cited by 41
Do Explanations make VQA Models more Predictable to a Human?Preprint
Chandrasekaran, A., Prabhu, V., Yadav, D., Chattopadhyay, P., & Parikh, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12366 · Cited by 41
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative PlayPreprint
Feng, S., & Boyd-Graber, J. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09648 · Cited by 37
Investigating User Confidence for Uncertainty Presentation in Predictive Decision Making
Arshad, S. Z., Zhou, J., Bridon, C., Chen, F., & Wang, Y. (2015) · Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction · doi:10.1145/2838739.2838753 · Cited by 30
McGrath, S., Mehta, P., Zytek, A., Lage, I., & Lakkaraju, H. (2020) · Trans. Mach. Learn. Res. · doi:10.48550/arxiv.2011.06167 · Cited by 30
Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism
Mallari, K., Inkpen, K., Johns, P., Tan, S., Ramesh, D., & Kamar, E. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376257 · Cited by 24
Progressive Disclosure: Designing for Effective TransparencyPreprint
Springer, A., & Whittaker, S. (2018) · arXiv.org · doi:10.48550/arxiv.1811.02164 · Cited by 24
Should We Trust (X)AI? Design Dimensions for Structured Experimental EvaluationsPreprint
Sperrle, F., El‐Assady, M., Guo, G., Chau, D. H., Endert, A., & Keim, D. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06433 · Cited by 20
Ghai, B., Liao, Q. V., Zhang, Y., Bellamy, R., & Mueller, K. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.09219 · Cited by 17
Self-citationGhosh, S., Liao, Q. V., Ramamurthy, K. N., Navrátil, J., Sattigeri, P., Varshney, K. R., & Zhang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01410 · Cited by 12
Self-citationHuman Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
Gonzalez, A. V., Bansal, G., Fan, A., Jia, R., Mehdad, Y., & Iyer, S. (2020) · arXiv · doi:10.48550/arxiv.2012.15075 · Cited by 7
Machine Explanations and Human Understanding
Chen, C., Feng, S., Sharma, A., & Tan, C. (2022) · arXiv · doi:10.48550/arxiv.2202.04092 · Cited by 6
Self-citation
Flagged first, then by citations
Research works printed but not linked: 16
- Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica. See https://www. propublica. org/article/machine-bias-risk-assessments-in-criminal-sentencing (2016).
- Suresh Kumar Annappindi. 2014. System and method for predicting consumer credit risk using income risk based credit score. US Patent 8,799,150.
- Lee J Cronbach and Paul E Meehl. 1955. Construct validity in psychological tests.Psychological bulletin 52, 4 (1955), 281.
- Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015. Algorithm aversion: People erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology: General 144, 1 (2015), 114.
- Steven E Dilsizian and Eliot L Siegel. 2014. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current cardiology reports 16, 1 (2014), 441.
- Julia Dressel and Hany Farid. 2018. The accuracy, fairness, and limits of predicting recidivism. Science advances 4, 1 (2018), eaao5580.
- Ben Green. 2021. The Flaws of Policies Requiring Human Oversight of Government Algorithms. Available at SSRN (2021).
- 10.2139/ssrn.3465622 · DOI · Paper page
- Charles H Lawshe. 1975. A quantitative approach to content validity. Personnel psychology 28, 4 (1975), 563–575.
- John D Lee and Katrina A See. 2004. Trust in automation: Designing for appropriate reliance. Human factors 46, 1 (2004), 50–80.
- Piyawat Lertvittayakumjorn and Francesca Toni. 2021. Explanation-Based Human Debugging of NLP Models: A Survey. arXiv preprint arXiv:2104.15135 (2021). · arXiv
- Han Liu, Vivian Lai, and Chenhao Tan. 2021. Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making. arXiv preprint arXiv:2101.05303 (2021). · arXiv
- Mahsan Nourani, Chiradeep Roy, Jeremy E Block, Donald R Honeycutt, Tahrima Rahman, Eric Ragan, and Vibhav Gogate. 2021. Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. In 26th International Conference on Intelligent User Interfaces. 340–350.
- Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018). · arXiv
- Peter Schmidt and Ann D Witte. 1988. Predicting recidivism in north carolina, 1978 and 1980. Inter-university Consortium for Political and Social Research.
- Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020. Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. arXiv preprint arXiv:2001.02114 (2020). · arXiv
Cited by
Sorted by citations
When combinations of humans and AI are useful: A systematic review and meta-analysis
Vaccaro, M., Almaatouq, A., & Malone, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02024-1 · Cited by 570
“This result complements the qualitative literature reviews on human–AI collaboration 27–29 , which highlight some of the surprising challenges that arise when integrating human intelligence and AI.”
The Metacognitive Demands and Opportunities of Generative AI
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642902 · Cited by 493
Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
Rong, Y., Leemann, T., Nguyen, T.-T., Fiedler, L., Qian, P., Unhelkar, V., Seidel, T., Kasneci, G., & Kasneci, E. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3331846 · Cited by 278
Exploring automation bias in human–AI collaboration: a review and implications for explainable AI
Romeo, G., & Conti, D. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02422-7 · Cited by 209
Kim, S. S. Y., Liao, Q. V., Vorvoreanu, M., Ballard, S., & Vaughan, J. W. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658941 · Cited by 147
Self-citation
Metadata
- Authors
- Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C.
- Affiliations
- University of Colorado Boulder (US)
- University of Chicago (US)
- Dataminr Inc., USA
- Microsoft (Canada) (CA)
- Venue
- 2023 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 1369-1385
- OpenAlex
- W4380365974
- Cited by
- 204
- References
- 77
- Citations by year
- 2026: 83 · 2025: 71 · 2024: 45 · 2023: 4
- FWCI
- 101.59
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- Funders
- National Science Foundation (2126602, 2040989)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- human-AI decision-making, AI assistance, research design, decision tasks, evaluation metrics, human-AI interaction, human-in-the-loop
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
