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Explaining models: an empirical study of how explanations impact fairness judgment
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Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need effective, unbiased, and user-friendly explanations that people can confidently rely on. Towards that end, we conducted an empirical study with four types of programmatically generated explanations to understand how they impact people's fairness judgments of ML systems. With an experiment involving more than 160 Mechanical Turk workers, we show that: 1) Certain explanations are considered inherently less fair, while others can enhance people's confidence in the fairness of the algorithm; 2) Different fairness problems-such as model-wide fairness issues versus case-specific fairness discrepancies-may be more effectively exposed through different styles of explanation; 3) Individual differences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to different styles of explanation. We conclude with a discussion on providing personalized and adaptive explanations to support fairness judgments of ML systems.
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“They found that local explanations, by highlighting unfair features used for individual predictions, appear to be more alarming than global explanations when used to explain an unfair model's decisions, and thus more effective in calibrating people's fairness judgment of ML models.”
From Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making (2020) · cites this paper as [8]
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“Tasks that involve inherent uncertainty are often those where humans tend to rely on AI systems for advice, such as loan approval [27, 34, 124], recidivism prediction [31, 47, 86], house price estimation [2, 13, 25], and student admission [13, 24].”
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 [31]
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Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · cited 2×
“Much recent research is dedicated to the generation of explanations in various styles, includingmodel-agnostic approaches [26, 30] applicable to any ML algorithm.”
BackgroundMethod"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 · cited 3×
“For example, LIME explains feature contribution by what happens to the prediction when the feature-values change (perturbing data) [30].”
BackgroundMethodGauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623
“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
Explanation in Artificial Intelligence: Insights from the Social Sciences
Miller, T. (2017) · arXiv · doi:10.48550/arxiv.1706.07269 · Cited by 5,338 · cited 1×
“However, this growing body of research is criticized for łapproaching this [XAI] challenge in a vacuum considering only the computational problemsž [27] without the quintessential understanding of how people perceive and use the explanations.”
BackgroundExplanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsPreprint
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03657 · Cited by 3,557
The Efficient Assessment of Need for Cognition
Cacioppo, J. T., Petty, R. E., & Feng Kao, C. (1984) · Journal of Personality Assessment · doi:10.1207/s15327752jpa4803_13 · Cited by 2,913 · cited 2×
“For cognitive style, we measure individual’s need for cognition [7].”
MethodLee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338 · cited 1×
“There has been a recent resurgence of XAI work driven by the challenge to interpret increasingly complex ML models, such as multi-layered neural networks, and by the evidence that ethical concerns and lack of trust hampers adoption of AI applications [15, 22].”
Background(2013) · Cited by 1,126
Cited by
Sorted by citations
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Algorithmic bias: review, synthesis, and future research directions
Kordzadeh, N., & Ghasemaghaei, M. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1927212 · Cited by 770
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
Liao, Q. V., Gruen, D., & Miller, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376590 · Cited by 925
Self-citationZhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372852 · Cited by 895
Self-citationManipulating and Measuring Model Interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
Expanding Explainability: Towards Social Transparency in AI systems
Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445188 · Cited by 538
Self-citationA Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods
Speith, T. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3534639 · Cited by 424
Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022) · Big Data & Society · doi:10.1177/20539517221115189 · Cited by 316
“In this reasoning, fairness can be achieved by decreasing “discriminatory consequences for certain groups of individuals” (Dodge et al., 2019: 275).”
BackgroundResultInfluentialWang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. S. (2024) · ACM Computing Surveys · doi:10.1145/3677119 · Cited by 723
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267
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
Haque, A. B., Islam, A. K. M. N., & Mikalef, P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122120 · Cited by 314
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
Lee, M. K., Kusbit, D., Kahng, A., Kim, J. T., Yuan, X., Chan, A., See, D., Noothigattu, R., Lee, S., Psomas, A., et al. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359283 · Cited by 260
Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594087 · Cited by 204
Self-citationJacobs, 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
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Angerschmid, A., Zhou, J., Theuermann, K., Chen, F., & Holzinger, A. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020026 · Cited by 161
“Dodge et al. [24] argued that AI explanations can also provide an effective interface for the human-in-the-loop, enabling people to identify and address fairness issues.”
BackgroundMethodInfluentialHow to explain AI systems to end users: a systematic literature review and research agenda
Laato, S., Tiainen, M., Najmul Islam, A. K. M., & Mäntymäki, M. (2022) · Internet Research · doi:10.1108/intr-08-2021-0600 · Cited by 155
Keane, M. T., & Smyth, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58342-2_11 · Cited by 158
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Chen, V., Liao, Q. V., Wortman Vaughan, J., & Bansal, G. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610219 · Cited by 220
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Kuppa, A., & Le‐Khac, N. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3117075 · Cited by 151
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Lai, V., Carton, S., Bhatnagar, R., Liao, Q. V., Zhang, Y., & Tan, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501999 · Cited by 145
Self-citationEthical machines: The human-centric use of artificial intelligence
Lepri, B., Oliver, N., & Pentland, A. (2021) · iScience · doi:10.1016/j.isci.2021.102249 · Cited by 149
A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
Kaluarachchi, T. I., Reis, A., & Nanayakkara, S. C. (2021) · Sensors · doi:10.3390/s21072514 · Cited by 135
Human-XAI Interaction: A Review and Design Principles for Explanation User Interfaces
Chromik, M., & Butz, A. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85616-8_36 · Cited by 147
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.-H., Muller, M., & Riedl, M. O. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642474 · Cited by 123
Self-citationSmith-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
Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445088 · Cited by 117
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Ehsan, U., Wintersberger, P., Liao, Q. V., Watkins, E. A., Manger, C., Daumé III, H., Riener, A., & Riedl, M. O. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503727 · Cited by 117
Self-citationExploring Explainability: A Definition, a Model, and a Knowledge Catalogue
Chazette, L., Brunotte, W., & Speith, T. (2021) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re51729.2021.00025 · Cited by 142
Operationalizing Human-Centered Perspectives in Explainable AI
Ehsan, U., Wintersberger, P., Liao, Q. V., Mara, M., Streit, M., Wachter, S., Riener, A., & Riedl, M. O. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3441342 · Cited by 121
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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-citationInstance-Based Counterfactual Explanations for Time Series Classification
Delaney, E. D., Greene, D., & Keane, M. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86957-1_3 · Cited by 109
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Kenny, E. M., & Keane, M. T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17377 · Cited by 108
Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 103
Anik, 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
Ochmann, J., Michels, L., Tiefenbeck, V., Maier, C., & Laumer, S. (2024) · Information Systems Journal · doi:10.1111/isj.12482 · Cited by 132
“This notion is further supported by prior research that obtained empirical evidence for the importance of transparency for algorithmic fairness perceptions (Binns et al., 2018; Dodge et al., 2019; Kizilcec, 2016).”
Result”Because AI is 100% right and safe”: User Attitudes and Sources of AI Authority in India
Kapania, S., Siy, O., Clapper, G., SP, A. M., & Sambasivan, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517533 · Cited by 90
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Ehsan, U., Saha, K., Choudhury, M. D., & Riedl, M. O. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579467 · Cited by 113
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Ferreira, J. J., & Monteiro, M. S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-49760-6_4 · Cited by 68
Travaini, G. V., Pacchioni, F., Bellumore, S., Bosia, M., & Micco, F. D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191710594 · Cited by 93
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Narayanan, D., Nagpal, M., McGuire, J., Schweitzer, S., & Cremer, D. D. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210890 · Cited by 99
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Zhang, J. M., & Harman, M. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00129 · Cited by 88
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Finocchiaro, J., Maio, R., Monachou, F., Patro, G. K., Raghavan, M., Stoica, A.-A., & Tsirtsis, S. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445912 · Cited by 89
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Wang, X., & Yin, M. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3519266 · Cited by 74
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Lund, B. D., Orhan, Z., Mannuru, N. R., Bevara, R. V. K., Porter, B., Vinaih, M. K., & Bhaskara, P. (2025) · AI and Ethics · doi:10.1007/s43681-025-00661-4 · Cited by 101
Yurrita, M., Draws, T., Balayn, A., Murray-Rust, D., Tintarev, N., & Bozzon, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581161 · Cited by 108
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Sikstrom, L., Maslej, M. M., Hui, K., Findlay, Z., Buchman, D. Z., & Hill, S. L. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100459 · Cited by 76
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Schoeffer, J., De‐Arteaga, M., & Kühl, N. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642621 · Cited by 105
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Kastner, L., Langer, M., Lazar, V., Schomacker, A., Speith, T., & Sterz, S. (2021) · 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew53955.2021.00031 · Cited by 61
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Nakao, Y., Stumpf, S., Ahmed, S., Naseer, A., & Strappelli, L. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3514258 · Cited by 69
Schoeffer, J., Kuehl, N., & Machowski, Y. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533218 · Cited by 67
Tal, A. S., Kuflik, T., & Kliger, D. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2095705 · Cited by 73
“We created the explanation styles used in the experiment based on the explanations presented in Binns et al. (2018) and Dodge et al. (2019), and according to the explanation creation guidelines in Gedikli et al. (2014).”
BackgroundMethodInfluentialBaroni, I., Re Calegari, G., Scandolari, D., & Celino, I. (2022) · Human Computation · doi:10.15346/hc.v9i1.134 · Cited by 65
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Melsión, G. I., Torre, I., Vidal, E., & Leite, I. (2021) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3459990.3460719 · Cited by 68
NICE: an algorithm for nearest instance counterfactual explanations
Brughmans, D., Leyman, P., & Martens, D. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00930-y · Cited by 64
Fairness and Decision-making in Collaborative Shift Scheduling Systems
Uhde, A., Schlicker, N., Wallach, D. P., & Hassenzahl, M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376656 · Cited by 64
Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems
Cheng, H.-F., Stapleton, L., Wang, R., Bullock, P., Chouldechova, A., Wu, Z. S. S., & Zhu, H. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445308 · Cited by 71
Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., & Dhurandhar, A. (2022) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v10i1.21995 · Cited by 69
Self-citationFairness via Explanation Quality
Dai, J. C., Upadhyay, S., Aïvodji, U., Bach, S. H., & Lakkaraju, H. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534159 · Cited by 72
Chiang, C.-W., Lu, Z., Li, Z., & Yin, M. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581015 · Cited by 72
Wintersberger, P., Nicklas, H., Martlbauer, T., Hammer, S., & Riener, A. (2020) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3409120.3410659 · Cited by 66
“Explanations can be global (explaining the whole system overall) or local (explanation from the perspective of a single instance) [5, 38], and various techniques (for example, Local Interpretable Model-agnostic Explanations LIME [29] or Shapley Additive Explanations SHAP [21])”
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Richardson, B., Garcia-Gathright, J. I., Way, S. F., Thom, J., & Cramer, H. S. M. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445604 · Cited by 67
Selective Explanations: Leveraging Human Input to Align Explainable AI
Lai, V. W.-M., Zhang, Y., Chen, C., Liao, Q. V., & Tan, C. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610206 · Cited by 75
Self-citationTal, A. S., Kuflik, T., & Kliger, D. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09623-4 · Cited by 59
“We choose to examine the following four basic explanation styles, as suggested by (Binns et al., 2018; Dodge et al., 2019) (presented in “Styles of black-box explanation” section): Case-based, Demographic-based, Input Influence-based and Sensitivity-based.”
BackgroundMethodResultInfluentialTowards Explainability for AI Fairness
Zhou, J. J., Chen, F., & Holzinger, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_18 · Cited by 56
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Gerdon, F., Bach, R. L., Kern, C. A., & Kreuter, F. (2022) · Big Data & Society · doi:10.1177/20539517221089305 · Cited by 71
“In addition, general trust in ML systems and the features used and not used are relevant for fairness judgments (Dodge et al., 2019).”
BackgroundA Critical Survey on Fairness Benefits of Explainable AI
Deck, L., Schoeffer, J., De‐Arteaga, M., & Kühl, N. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658990 · Cited by 107
Nakao, Y., Strappelli, L., Stumpf, S., Naseer, A., Regoli, D., & Gamba, G. D. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2067936 · Cited by 66
“Coupled with data visualisation, XAI approaches are often used to identify potential bias, for example, Dodge et al. (Dodge et al., 2019) investigated global and local explanation styles in fairness assessments.”
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Gómez, C., Unberath, M., & Huang, C. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102977 · Cited by 57
Al-Ansari, N., Al‐Thani, D., & Al-Mansoori, R. S. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/4628855 · Cited by 58
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Lee, M. K., Grgić-Hlača, N., Tschantz, M. C., Binns, R., Weller, A., Carney, M., & Inkpen, K. M. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3375158 · Cited by 57
Naveed, S., Stevens, G., & Robin-Kern, D. (2024) · Applied Sciences · doi:10.3390/app142311288 · Cited by 67
Huynh, M.-T., & Aichner, T. (2025) · AI & Society · doi:10.1007/s00146-025-02378-8 · Cited by 69
Ma, S., Chen, Q., Wang, X., Zheng, C., Peng, Z., Yin, M., & Ma, X. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713423 · Cited by 48
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“Chen et al and Lim et al. showed that users have different information needs [9, 33], or users may request multiple information content from the system [13].”
BackgroundTowards a multi-stakeholder value-based assessment framework for algorithmic systems
Yurrita, M., Murray-Rust, D., Balayn, A., & Bozzon, A. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533118 · Cited by 50
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Self-citationSalimzadeh, S., He, G., & Gadiraju, U. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641905 · Cited by 41
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“This assessment is affected by explanation styles as certain explanations are inherently less fair while some can enhance confidence in the algorithmic fairness (Dodge et al., 2019).”
BackgroundCategorical and Continuous Features in Counterfactual Explanations of AI Systems
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Metadata
- Authors
- Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., & Dugan, C.
- Affiliations
- Oregon State University (US)
- IBM (United States) (US)
- Venue
- Proceedings of the 24th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2019-03-17
- Pages
- 275-285
- OpenAlex
- W4288414189
- Cited by
- 286
- References
- 43
- Citations by year
- 2026: 24 · 2025: 44 · 2024: 52 · 2023: 48 · 2022: 56 · 2021: 41 · 2020: 18 · 2019: 3
- FWCI
- 11.04
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Army Research Laboratory and UK Ministry of Defence (W911NF-16-3-0001)
- Defense Advanced Research Projects Agency (N66001-17-2-4030)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- explainable AI, algorithmic fairness, fairness judgments, human-in-the-loop, explanation styles, individual differences, personalized explanations, adaptive explanations, trust in machine learning
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.
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