The effects of example-based explanations in a machine learning interface
Open accessAbstract
The black-box nature of machine learning algorithms can make their predictions difficult to understand and explain to end-users. In this paper, we propose and evaluate two kinds of example-based explanations in the visual domain, normative explanations and comparative explanations (Figure 1), which automatically surface examples from the training set of a deep neural net sketch-recognition algorithm. To investigate their effects, we deployed these explanations to 1150 users on QuickDraw, an online platform where users draw images and see whether a recognizer has correctly guessed the intended drawing. When the algorithm failed to recognize the drawing, those who received normative explanations felt they had a better understanding of the system, and perceived the system to have higher capability. However, comparative explanations did not always improve perceptions of the algorithm, possibly because they sometimes exposed limitations of the algorithm and may have led to surprise. These findings suggest that examples can serve as a vehicle for explaining algorithmic behavior, but point to relative advantages and disadvantages of using different kinds of examples, depending on the goal.
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Other studies suggest example-based explanations are better at helping people detect model errors, usually by recognizing the dissimilarity between the instance and selected examples.
“Wang and Yin [82] found that example-based explanations underperform feature-based explanations for improving appropriate reliance, though other studies suggest example-based explanations are better at helping people detect model errors [11,13,49], usually by recognizing the dissimilarity between the instance and selected examples.”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [13]
The Results and Discussion (sections 8–9; page numbers unavailable) suggest that comparative examples sometimes exposed algorithmic limitations. But the study did not measure whether people detected errors better with example-based explanations, and it attributes the surprise to examples that were visually similar but semantically unrelated—not usually to dissimilarity between the drawing and selected examples. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCai et al. [3] found that comparative explanation can lead to better discovery of the limitations of the AI, compared to normative explanation.
“A study conducted by Cai et al. [3] found that comparative explanation, by comparisons with examples in alternative classes, can lead to better discovery of the limitations of the AI, compared to normative explanation that describes examples in the intended class.”
From Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making (2020) · cites this paper as [3]
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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 · see citing works
An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736 · see citing works
Anchors: 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 1× · see citing works
“Examples can potentially help users develop intuition for the reasons underlying machine predictions, without directly exposing the internal logic of the algorithm [19, 28, 33].”
BackgroundEuropean Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131 · see citing works
Explaining collaborative filtering recommendations
Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000) · Proceedings of the 2000 ACM conference on Computer supported cooperative work · doi:10.1145/358916.358995 · Cited by 1,731 · see citing works
Understanding Black-box Predictions via Influence Functions
Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1703.04730 · Cited by 1,514 · see citing works
The Psychology of Social Norms.
Hankins, F. H., & Sherif, M. (1937) · American Sociological Review · doi:10.2307/2084385 · Cited by 1,470 · see citing works
SmoothGrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv · doi:10.48550/arxiv.1706.03825 · Cited by 1,467 · see citing works
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., & Sayres, R. (2017) · arXiv · doi:10.48550/arxiv.1711.11279 · Cited by 1,240 · see citing works
Self-citationThe attribution of blame : causality, responsibility, and blameworthiness
Shaver, K. G. (1985) · Cited by 800 · see citing works
Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174156 · Cited by 743 · see citing works
How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
Kizilcec, R. F. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858402 · Cited by 615 · see citing works
<i>Why and why not</i> 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 · see citing works
Toward an Instructionally Oriented Theory of Example‐Based Learning
Renkl, A. (2014) · Cognitive Science · doi:10.1111/cogs.12086 · Cited by 509 · see citing works
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 · see citing works
Self-citationExplanations 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 · see citing works
First I "like" it, then I hide it: Folk Theories of Social Feeds
Eslami, M., Karahalios, K., Sandvig, C., Vaccaro, K., Rickman, A., Hamilton, K., & Kirlik, A. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858494 · Cited by 375 · see citing works
Trust building with explanation interfaces
Pu, P., & Chen, L. (2006) · Proceedings of the 11th international conference on Intelligent user interfaces · doi:10.1145/1111449.1111475 · Cited by 300 · see citing works
Explanatory coherence in social explanations: A parallel distributed processing account.
Read, S. J., & Marcus‐Newhall, A. (1993) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.65.3.429 · Cited by 279 · see citing works
Bringing Transparency Design into Practice
Eiband, M., Schneider, H., Bilandzic, M., Fazekas-Con, J., Haug, M., & Hussmann, H. (2018) · 23rd International Conference on Intelligent User Interfaces · doi:10.1145/3172944.3172961 · Cited by 250 · see citing works
Assessing demand for intelligibility in context-aware applications
Lim, B. Y., & Dey, A. K. (2009) · Proceedings of the 11th international conference on Ubiquitous computing · doi:10.1145/1620545.1620576 · Cited by 246 · see citing works
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 · see citing works
Attribution theories: How people make sense of behavior.
Malle, B. F. (2011) · Cited by 195 · see citing works
Communicating Algorithmic Process in Online Behavioral Advertising
Eslami, M., Krishna Kumaran, S. R., Sandvig, C., & Karahalios, K. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174006 · Cited by 171 · see citing works
Multi-Language Online Handwriting Recognition
Keysers, D., Deselaers, T., Rowley, H. A., Wang, L.-L., & Cărbune, V. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572693 · Cited by 167 · see citing works
Example-Based Learning in Heuristic Domains: A Cognitive Load Theory Account
Renkl, A., Hilbert, T. S., & Schworm, S. (2008) · Educational Psychology Review · doi:10.1007/s10648-008-9093-4 · Cited by 136 · see citing works
Schaffer, J., Giridhar, P., Jones, D. G., Höllerer, T., Abdelzaher, T., & O’Donovan, J. (2015) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2678025.2701406 · Cited by 39 · see citing works
Determinants of Judgments of Explanatory Power: Credibility, Generality, and Statistical Relevance
Colombo, M., Bucher, L., & Sprenger, J. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01430 · Cited by 25 · see citing works
Adapting arcade games for learning
Cai, C. J. (2013) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2468356.2479490 · Cited by 12 · see citing works
Self-citationExplaining Data-Driven Document Classifications
Martens, D., & Provost, F. J. (2013) · Faculty Digital Archive (New York University Florence) · Cited by 11 · see citing works
Flagged first, then by citations
2 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [6]6. Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2009. Visu- alizing higher-layer features of a deep network. University of Montreal 1341, 3 (2009), 1.
- [10]10. Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000. Explaining col- laborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work. ACM, 241-250.
- [21]21. Tim Miller. 2017. Explanation in artificial intelligence: insights from the social sciences. arXiv preprint arXiv:1706.07269 (2017). · arXiv
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 · see citing works
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 · see citing works
Zhang, 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 · see citing works
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785 · see citing works
Cai, C. J., Winter, S., Steiner, D., Wilcox, L., & Terry, M. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359206 · Cited by 594 · see citing works
Self-citationExpanding 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 · see citing works
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 456 · see citing works
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 · see citing works
Self-citationWang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320 · see citing works
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 · see citing works
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 · see citing works
Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103459 · Cited by 211 · see citing works
Human-centered XAI: Developing design patterns for explanations of clinical decision support systems
Schoonderwoerd, T. A. J., Jorritsma, W., Neerincx, M. A., & van den Bosch, K. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102684 · Cited by 210 · see citing works
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 · see citing works
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 · see citing works
Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200 · see citing works
Guidelines and evaluation of clinical explainable AI in medical image analysis
Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102684 · Cited by 199 · see citing works
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
Chatzimparmpas, A., Martins, R. M., Jusufi, I., Kucher, K., Fabrice Rossi, A., & Kerren, A. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14034 · Cited by 184 · see citing works
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102941 · Cited by 170 · see citing works
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 · see citing works
Fairness and Explanation in AI-Informed Decision Making
Angerschmid, A., Zhou, J., Theuermann, K., Chen, F., & Holzinger, A. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020026 · Cited by 161 · see citing works
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
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 145 · see citing works
“Wang and Yin [73] found that example-based explanations underperform feature-based explanations for improving appropriate reliance, though other studies suggest example-based explanations are better at helping people detect model errors [10, 13, 45], usually by recognizing the dissimilarity between the instance and se…”
BackgroundA 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 132 · see citing works
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 · see citing works
Ayano, Y. M., Schwenker, F., Dufera, B. D., & Debelee, T. G. (2022) · Diagnostics · doi:10.3390/diagnostics13010111 · Cited by 132 · see citing works
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 128 · see citing works
BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125 · see citing works
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 · see citing works
Exploring 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 120 · see citing works
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 · see citing works
Hua, D., Petrina, N., Young, N., Cho, J., & Poon, S. K.-S. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102698 · Cited by 117 · see citing works
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 · see citing works
Modeling adoption of intelligent agents in medical imaging
Calisto, F. M., Nunes, N., & Nascimento, J. C. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102922 · Cited by 111 · see citing works
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 · see citing works
Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis
Calisto, F. M., Fernandes, J., Morais, M., Santiago, C., Abrantes, J., Nunes, N., & Nascimento, J. C. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580682 · Cited by 106 · see citing works
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 · see citing works
The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice
Lehmann, C. A., Haubitz, C. B., Fügener, A., & Thonemann, U. W. (2022) · Production and Operations Management · doi:10.1111/poms.13770 · Cited by 100 · see citing works
A framework of artificial intelligence augmented design support
Liao, J., Hansen, P., & Chai, C. (2020) · Human-Computer Interaction · doi:10.1080/07370024.2020.1733576 · Cited by 100 · see citing works
Survey on Explainable AI: Techniques, challenges and open issues
Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98 · see citing works
COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Abdul, A., von der Weth, C., Kankanhalli, M., & Lim, B. Y. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376615 · Cited by 96 · see citing works
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102607 · Cited by 96 · see citing works
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Ehsan, U., Saha, K., Choudhury, M. D., & Riedl, M. O. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579467 · Cited by 92 · see citing works
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., & Vössing, M. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534128 · Cited by 91 · see citing works
You Complete Me: Human-AI Teams and Complementary Expertise
Zhang, Q., Lee, M. L., & Carter, S. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517791 · Cited by 85 · see citing works
XAIR: A Framework of Explainable AI in Augmented Reality
Xu, X., Yu, A., Jonker, T. R., Todi, K., Lu, F., Qian, X., Belo, J. M. E., Wang, T., Li, M., Mun, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581500 · Cited by 84 · see citing works
What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice
Ferreira, J. J., & Monteiro, M. D. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49760-6_4 · Cited by 83 · see citing works
Human-AI Collaboration in a Cooperative Game Setting
Ashktorab, Z., Liao, Q., Dugan, C., Johnson, J. M., Pan, Q., Zhang, W., Kumaravel, S., & Campbell, M. S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415167 · Cited by 83 · see citing works
Effects of Explanations in AI-Assisted Decision Making: Principles and Comparisons
Wang, X., & Yin, M. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3519266 · Cited by 82 · see citing works
Training data influence analysis and estimation: a survey
Hammoudeh, Z., & Lowd, D. (2024) · Machine Learning · doi:10.1007/s10994-023-06495-7 · Cited by 80 · see citing works
Khurana, A., Subramonyam, H., & Chilana, P. K. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645200 · Cited by 76 · see citing works
On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness
Kästner, L., Langer, M., Lazar, V., Schomäcker, A., Speith, T., & Sterz, S. (2021) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew53955.2021.00031 · Cited by 72 · see citing works
Mupangwa, W., Chipindu, L., Nyagumbo, I., Mkuhlani, S., & Sisito, G. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-2711-6 · Cited by 71 · see citing works
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 · see citing works
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Morrison, K., Spitzer, P., Turri, V., Feng, M., Kühl, N., & Perer, A. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3641022 · Cited by 69 · see citing works
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Ghai, B., & Mueller, K. D. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209484 · Cited by 68 · see citing works
Towards relatable explainable AI with the perceptual process
Zhang, W., & Lim, B. Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501826 · Cited by 67 · see citing works
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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 64 · see citing works
What is "intelligent" in intelligent user interfaces?
Völkel, S. T., Schneegass, C., Eiband, M., & Buschek, D. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377500 · Cited by 61 · see citing works
Naveed, S., Stevens, G., & Robin-Kern, D. (2024) · Applied Sciences · doi:10.3390/app142311288 · Cited by 54 · see citing works
Al-Ansari, N., Al‐Thani, D., & Al-Mansoori, R. S. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/4628855 · Cited by 52 · see citing works
Kim, T., & Song, H. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2049134 · Cited by 51 · see citing works
ChatrEx: Designing Explainable Chatbot Interfaces for Enhancing Usefulness, Transparency, and Trust
Khurana, A., Harjandi, P. A., & Chilana, P. K. (2021) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc51201.2021.9576440 · Cited by 49 · see citing works
AI in the Public Eye: Investigating Public AI Literacy Through AI Art
Hemment, D., Currie, M. E., Bennett, S., Elwes, J., Ridler, A., Sinders, C., Vidmar, M., Hill, R. L., & Warner, H. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594052 · Cited by 49 · see citing works
Du, Y., Antoniadi, A. M., McNestry, C., McAuliffe, F. M., & Mooney, C. (2022) · Applied Sciences · doi:10.3390/app122010323 · Cited by 47 · see citing works
Attitudes and Folk Theories of Data Subjects on Transparency and Accuracy in Emotion Recognition
Grill, G., & Andalibi, N. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512925 · Cited by 47 · see citing works
AI should embody our values: Investigating journalistic values to inform AI technology design
Komatsu, T., Lopez, M. G., Makri, S., Porlezza, C., Cooper, G., MacFarlane, A., & Missaoui, S. (2020) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3419249.3420105 · Cited by 45 · see citing works
Explainable Artificial Intelligence in Cybersecurity: A Brief Review
Hariharan, S., Velicheti, A., Anagha, A. S., Thomas, C., & Balakrishnan, N. (2021) · doi:10.1109/isea-isap54304.2021.9689765 · Cited by 44 · see citing works
Explainable software systems: from requirements analysis to system evaluation
Chazette, L., Brunotte, W., & Speith, T. (2022) · Requirements Engineering · doi:10.1007/s00766-022-00393-5 · Cited by 44 · see citing works
Leichtmann, B., Hinterreiter, A., Humer, C., Streit, M., & Mara, M. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2221605 · Cited by 44 · see citing works
Personalising Explainable Recommendations: Literature and Conceptualisation
Naiseh, M., Jiang, N., Ma, J., & Ali, R. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-45691-7_49 · Cited by 42 · see citing works
A Missing Piece in the Puzzle: Considering the Role of Task Complexity in Human-AI Decision Making
Salimzadeh, S., He, G., & Gadiraju, U. (2023) · Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization · doi:10.1145/3565472.3592959 · Cited by 42 · see citing works
Towards 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 42 · see citing works
Salimzadeh, 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 · see citing works
“According to Lai et al. [69], tasks can also be differentiated based on whether they are emulating human intelligence, like object recognition [20], or based on discovered patterns in data such as recidivism prediction [86].”
BackgroundRezaeian, O., Bayrak, A. E., & Asan, O. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2539458 · Cited by 39 · see citing works
Rezaeian, O., Bayrak, A. E., & Asan, O. (2025) · arXiv · doi:10.48550/arxiv.2501.16693 · Cited by 39 · see citing works
Yeh, S.-F., Wu, M.-H., Chen, T.-Y., Lin, Y.-C., Chang, X.-J., Chiang, Y.-H., & Chang, Y.-J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501941 · Cited by 38 · see citing works
Evaluating the impact of human explanation strategies on human-AI visual decision-making
Morrison, K., Shin, D., Holstein, K., & Perer, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579481 · Cited by 37 · see citing works
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Robbemond, V., Inel, O., & Gadiraju, U. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3503252.3531311 · Cited by 36 · see citing works
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Chu, E., Roy, D., & Andreas, J. (2020) · arXiv · doi:10.48550/arxiv.2007.12248 · Cited by 34 · see citing works
Pumplun, L., Peters, F., Gawlitza, J. F., & Buxmann, P. (2023) · Journal of the Association for Information Systems · doi:10.17705/1jais.00820 · Cited by 32 · see citing works
Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations
Cau, F. M., Hauptmann, H., Spano, L. D., & Tintarev, N. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584080 · Cited by 31 · see citing works
Toward Human-centered XAI in Practice: A survey
Kong, X., Liu, S., & Zhu, L. (2024) · Machine Intelligence Research · doi:10.1007/s11633-022-1407-3 · Cited by 31 · see citing works
Aquilino, L., Dio, C. D., Manzi, F., Massaro, D., Bisconti, P., & Marchetti, A. (2025) · Informatics · doi:10.3390/informatics12030070 · Cited by 30 · see citing works
Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis
Schoop, E., Zhou, X., Li, G., Chen, Z., Hartmann, B., & Li, Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517497 · Cited by 30 · see citing works
Kaate, I., Salminen, J. O., Jung, S., Xuan, T. T. T., Häyhänen, E., Azem, J. Y., & Jansen, B. J. J. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712160 · Cited by 29 · see citing works
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 · see citing works
Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine
Obuchowicz, R., Piórkowski, A., Nurzyǹska, K., Obuchowicz, B., Strzelecki, M., & Bielecka, M. (2026) · Diagnostics · doi:10.3390/diagnostics16030396 · Cited by 26 · see citing works
Liefgreen, A., Weinstein, N., Wachter, S., & Mittelstadt, B. (2023) · AI & Society · doi:10.1007/s00146-023-01684-3 · Cited by 26 · see citing works
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 25 · see citing works
Corti, L., Oltmans, R., Jung, J., Balayn, A., Wijsenbeek, M. S., & Yang, J. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642551 · Cited by 24 · see citing works
Artificial Intelligence—A New Knowledge and Decision-Making Paradigm?
Huang, L., & Peissl, W. (2023) · Springer eBooks · doi:10.1007/978-3-031-10617-0_9 · Cited by 24 · see citing works
Counterfactual explanations for misclassified images: How human and machine explanations differ
Delaney, E. D., Pakrashi, A., Greene, D., & Keane, M. T. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103995 · Cited by 24 · see citing works
SoK: Explainable Machine Learning in Adversarial Environments
Noppel, M., & Wressnegger, C. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00021 · Cited by 24 · see citing works
Explaining Multimodal Deceptive News Prediction Models
Volkova, S., Ayton, E., Arendt, D., Huang, Z., & Hutchinson, B. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3266 · Cited by 23 · see citing works
Bove, C., Lesot, M., Tijus, C., & Detyniecki, M. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584082 · Cited by 23 · see citing works
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 22 · see citing works
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 22 · see citing works
A social evaluation of the perceived goodness of explainability in machine learning
Wanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2021) · Journal of Business Analytics · doi:10.1080/2573234x.2021.1952913 · Cited by 22 · see citing works
Theophilou, E., Lomonaco, F., Donabauer, G., Ognibene, D., Sánchez-Reina, J. R., & Hernández‐Leo, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42682-7_28 · Cited by 22 · see citing works
- Authors
- Cai, C. J., Jongejan, J., & Holbrook, J.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 24th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2019-03-17
- Pages
- 258-262
- OpenAlex
- W2916797267
- Cited by
- 220
- References
- 32
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 42 · 2023: 38 · 2022: 35 · 2021: 34 · 2020: 28 · 2019: 4
- FWCI
- 9.66
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- example-based explanations, explainable machine learning, normative explanation, sketch recognition, deep neural network, black-box algorithms, QuickDraw
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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