Cited by 220Explainable Artificial Intelligence (XAI)Data Visualization and AnalyticsUsability and User Interface Design

The DOI matches its Crossref record; 30 of the 32 references it cites were found in a registry or an index.

The effects of example-based explanations in a machine learning interface

Open access

Cai, Carrie J., Jongejan, Jonas, Holbrook, Jess·2019·Proceedings of the 24th International Conference on Intelligent User Interfaces

DOI 10.1145/3301275.3302289

Open this paper in CoChat — free account Open PDF

Abstract

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.

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

Crossref · 10.1145/3301275.3302289

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

"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
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
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×
“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].”
Background
European 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 · cited 1×
“The size, complexity, and opacity of ML algorithms have led to increasing regulations and demands for more explainable AI [1, 9].”
Background
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
Understanding Black-box Predictions via Influence Functions
Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1703.04730 · Cited by 1,514
The Psychology of Social Norms.
Hankins, F. H., & Sherif, M. (1937) · American Sociological Review · doi:10.2307/2084385 · Cited by 1,468
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
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
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
Self-citation
The attribution of blame : causality, responsibility, and blameworthiness
Shaver, K. G. (1985) · Cited by 800

Flagged first, then by citations

Show all 32 references

2 of the 32 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

Dumitru Erhan , Yoshua Bengio , Aaron Courville , and Pascal Vincent . 2009 . Visualizing higher-layer features of a deep network . University of Montreal 1341 , 3 (2009), 1 . Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2009. Visualizing higher-layer features of a deep network. University of Montreal 1341, 3 (2009), 1.
Tim Miller . 2017. Explanation in artificial intelligence: insights from the social sciences. arXiv preprint arXiv:1706.07269 ( 2017 ). Tim Miller. 2017. Explanation in artificial intelligence: insights from the social sciences. arXiv preprint arXiv:1706.07269 (2017). · arXiv
Sandra Wachter Brent Mittelstadt and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. (2017). Sandra Wachter Brent Mittelstadt and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. (2017). · DOI · Paper page

Sorted by citations

Items 101–200 of 220

Algorithmic and HCI Aspects for Explaining Recommendations of Artistic Images
Dominguez, V., Donoso-Guzmán, I., Messina, P., & Parra, D. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3369396 · Cited by 22
Designing an XAI interface for BCI experts: A contextual design for pragmatic explanation interface based on domain knowledge in a specific context
Kim, S., Choo, S., Park, D., Park, H., Nam, C. S., Jung, J., & Lee, S. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103009 · Cited by 23
"If it is easy to understand then it will have value": Examining Perceptions of Explainable AI with Community Health Workers in Rural India
Okolo, C. T., Agarwal, D., Dell, N. L., & Vashistha, A. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637348 · Cited by 28
The impact of AI explanations on clinicians’ trust and diagnostic accuracy in breast cancer
Rezaeian, O., Asan, O., & Bayrak, A. E. (2025) · Applied Ergonomics · doi:10.1016/j.apergo.2025.104577 · Cited by 23
View-based Explanations for Graph Neural Networks
Chen, T., Qiu, D., Wu, Y., Khan, A., Ke, X., & Gao, Y. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3639295 · Cited by 20
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
Do Expressions Change Decisions? Exploring the Impact of AI's Explanation Tone on Decision-Making
Okoso, A., Yang, M., & Baba, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713744 · Cited by 26
Amplifying Domain Expertise in Clinical Data Pipelines
Rahman, P., Nandi, A., & Hebert, C. L. (2020) · JMIR Medical Informatics · doi:10.2196/19612 · Cited by 19
From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein’s Theory of Explanation
Sovrano, F., & Vitali, F. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450655 · Cited by 19
Question-Driven Design Process for Explainable AI User Experiences
Liao, Q. V., Pribić, M., Han, J., Miller, S., & Sow, D. (2021) · arXiv · doi:10.48550/arxiv.2104.03483 · Cited by 18
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Workers Through Explainable Artificial Intelligence
Schemmer, M., Kühl, N., & Satzger, G. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.185 · Cited by 18
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity RecognitionPreprint
Nourani, M., Roy, C., Rahman, T., Ragan, E. D., Ruozzi, N., & Gogate, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02335 · Cited by 24
How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?Preprint
Yang, F. (2021) · doi:10.31219/osf.io/s9q8t · Cited by 18
Transparent-AI Blueprint: Developing a Conceptual Tool to Support the Design of Transparent AI Agents
Zhou, Z., Li, Z., Zhang, Y., & Sun, L. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2093773 · Cited by 18
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis SystemsPreprint
Lucieri, A., Bajwa, M. N., Dengel, A. R., & Ahmed, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13169 · Cited by 20
The Who in Explainable AI: How AI Background Shapes Perceptions of AI ExplanationsPreprint
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.-H., Muller, M. J., & Riedl, M. O. (2021) · arXiv (Cornell University) · Cited by 17
Operationalizing selective transparency using progressive disclosure in artificial intelligence clinical diagnosis systems
Muralidhar, D., Belloum, R., & Ashok, A. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103591 · Cited by 21
A User Interface for Explaining Machine Learning Model Explanations
Kadir, M. A., Selim, A. M., Barz, M., & Sonntag, D. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581754.3584131 · Cited by 16
Categorical and Continuous Features in Counterfactual Explanations of AI Systems
Warren, G., Byrne, R. M. J., & Keane, M. T. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3673907 · Cited by 16
Development and translation of human-AI interaction models into working prototypes for clinical decision-making
Hussain, M. H., Iacovides, I., Lawton, T., Sharma, V., Porter, Z., Cunningham, A., Habli, I., Hickey, S., Jia, Y., Morgan, P., et al. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660697 · Cited by 18
Framework for human–XAI symbiosis: extended self from the dual-process theory perspective
Litvinova, Y., Mikalef, P., & Luo, X. R. (2024) · Journal of Business Analytics · doi:10.1080/2573234x.2024.2396366 · Cited by 16
The Explanation That Hits Home: The Characteristics of Verbal Explanations That Affect Human Perception in Subjective Decision-Making
Ferguson, S. A., Aoyagui, P. A., Rizvi, R., Kim, Y., & Kuzminykh, A. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687056 · Cited by 17
Effect of Confidence Indicators on Trust in AI-Generated Profiles
Bruzzese, T., Gao, I., Dietz, G., Ding, C., & Romanos, A. B. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3382842 · Cited by 18
Non-monotonic Explanation Functions
Leila Amgoud, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86772-0_2 · Cited by 14
Guided By AI: Navigating Trust, Bias, and Data Exploration in AI‐Guided Visual Analytics
Ha, S., Monadjemi, S., & Ottley, A. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15108 · Cited by 19
Comparative Study of XAI Perception Between Eastern and Western Cultures
Chien, S.-Y. J., Wang, Y.-F., Cheng, K., & Chen, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2441015 · Cited by 16
AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge using Feature Importance Visualization
Velumani, R. P., Xia, M., Han, J., Wang, C., Lau, A. K. H., & Qu, H. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511150 · Cited by 12
Why the Fine, AI? The Effect of Explanation Level on Citizens' Fairness Perception of AI-based Discretion in Public Administrations
Aljuneidi, S., Heuten, W., Abdenebaoui, L., Wolters, M. K., & Boll, S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642535 · Cited by 15
Explainable AI for Audio and Visual Affective Computing: A Scoping Review
Johnson, D. S., Hakobyan, O., Paletschek, J., & Drimalla, H. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3505269 · Cited by 14
Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis
Lim, B. Y., Cahaly, J. P., Sng, C. Y. F., & Chew, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714058 · Cited by 12
Effects of Fairness and Explanation on Trust in Ethical AI
Angerschmid, A., Theuermann, K., Holzinger, A., Chen, F., & Zhou, J. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14463-9_4 · Cited by 14
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual ExamplesPreprint
Myers, C. M., Freed, E., Pardo, L. L., Furqan, A., Risi, S., & Zhu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.02271 · Cited by 14
RelatIF: Identifying Explanatory Training Examples via Relative InfluencePreprint
Barshan, E., Brunet, M.-E., & Dziugaite, G. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11630 · Cited by 17
Transparent internal human-machine interfaces in highly automated shuttles to support the communication of minimal risk maneuvers to the passengers
Brandt, T., Wilbrink, M., & Oehl, M. (2024) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2024.09.006 · Cited by 17
Human performance consequences of normative and contrastive explanations: An experiment in machine learning for reliability maintenance
Gentile, D., Donmez, B., & Jamieson, G. A. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103945 · Cited by 13
Impact of example-based XAI for neural networks on trust, understanding, and performance
Perlmutter, M., Gifford, R. C., & Krening, S. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103277 · Cited by 12
To Use or Not to Use: Impatience and Overreliance When Using Generative AI Productivity Support Tools
Qiao, H., Vermeulen, J., Fitzmaurice, G. W., & Matejka, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714103 · Cited by 15
Explainable AI for online disinformation detection: Insights from a design science research project
Bezzaoui, I., Stein, C., Weinhardt, C., & Fegert, J. (2025) · Electronic Markets · doi:10.1007/s12525-025-00799-3 · Cited by 12
Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning
Kim, C., Xiao, L., Collins, C. M., Taylor, G. W., & Amer, M. R. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3465407 · Cited by 10
The role of artificial intelligence in drying and biomass valorization in the field of phytoremediation of contaminated soils
Singh, P., Pani, A., MUJUMDAR, A. S., & Shirkole, S. S. (2024) · Drying Technology · doi:10.1080/07373937.2024.2345123 · Cited by 9
Exploring the impact of explainable AI and cognitive capabilities on users’ decisions
Cau, F. M., & Spano, L. D. (2025) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-025-09438-0 · Cited by 16
Exploring Users’ Preferences for Chatbot’s Guidance Type and Timing
Wu, M.-H., Yeh, S.-F., Chang, X.-J., & Chang, Y.-J. (2021) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3462204.3481756 · Cited by 9
Pilot Attitudes Toward AI in the Cockpit: Implications for Design
Zhang, Z. T., Liu, Y., & Husmann, H. (2021) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms53169.2021.9582448 · Cited by 11
It’s just a recipe?—Comparing expert and lay user understanding of algorithmic systems.
Ngo, T., & Krämer, N. C. (2021) · Technology Mind and Behavior · doi:10.1037/tmb0000045 · Cited by 12
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial IntelligencePreprint
Schemmer, M., Kühl, N., & Satzger, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.13827 · Cited by 9
The Influence of Curiosity Traits and On-Demand Explanations in AI-Assisted Decision-Making
Cau, F. M., & Spano, L. D. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712165 · Cited by 13
DeepSeer: Interactive RNN Explanation and Debugging via State Abstraction
Wang, Z., Huang, Y., Song, D., Ma, L., & Zhang, T. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580852 · Cited by 8
Human-Computer Interaction in Intelligent Environments
Stephanidis, C., & Salvendy, G. (2024) · CRC Press eBooks · doi:10.1201/9781003490685 · Cited by 8
EUCA: A Practical Prototyping Framework towards End-User-Centered Explainable Artificial Intelligence.Preprint
Jin, W., Fan, J., Gromala, D., Pasquier, P., & Hamarneh, G. (2021) · arXiv (Cornell University) · Cited by 8
Human performance effects of combining counterfactual explanations with normative and contrastive explanations in supervised machine learning for automated decision assistance
Gentile, D., Donmez, B., & Jamieson, G. A. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103434 · Cited by 8
Scalable Agile Framework for Execution in AI for Medical AI Ethics Policy Design in Small- and Medium-Sized Enterprises
Nemteanu, I., Mancebo, A., Jr., Joe, L., Lopez, R., Lopez, P., & Pettine, W. W. (2025) · Journal of Medical Internet Research · doi:10.2196/80028 · Cited by 7
An Architecture to Support Graduated Levels of Trust for Cancer Diagnosis with AI
Rezaeian, O., Bayrak, A. E., & Asan, O. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-61966-3_37 · Cited by 7
Explaining instances in the health domain based on the exploration of a dataset's hardness embedding
Valeriano, M. G., Pereira, J. L. J., Kiffer, C. R. V., & Lorena, A. C. (2024) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3638530.3664113 · Cited by 7
Context sight
Yuan, J., & Bertini, E. (2022) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/3546930.3547502 · Cited by 7
Dissecting users' needs for search result explanations
Juneja, P., Zhang, W., Smith, A., Lamba, H., Tetreault, J., & Jaimes, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642059 · Cited by 14
How Do People Rank Multiple Mutant Agents?
Dodge, J. E., Anderson, A. A., Olson, M., Dikkala, R., & Burnett, M. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511115 · Cited by 7
Safe Environments to Understand Medical AI - Designing a Diabetes Simulation Interface for Users of Automated Insulin Delivery
Schrills, T., Gruner, M., Peuscher, H., & Franke, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35748-0_23 · Cited by 7
Contextualising local explanations for non-expert users: an XAI pricing interface for insurancePreprint
Bove, C., Aigrain, J., Lesot, M., Tijus, C., & Detyniecki, M. (2021) · HAL · Cited by 6
Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making
Lee, M. H., & Tok, M. Z. Y. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732155 · Cited by 8
Capturing the Trends, Applications, Issues, and Potential Strategies of Designing Transparent AI Agents
Sun, L., Li, Z., Zhang, Y., Liu, Y., Lou, S., & Zhou, Z. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451819 · Cited by 7
Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems
Do, H. J., Brachman, M., Dugan, C., Pan, Q., Rai, P., Johnson, J. M., & Thawani, R. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686912 · Cited by 7
AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multimodal Information Between Reality and Videos
Ning, Z., Li, L., Killough, D., Seo, J., Carrington, P., Tian, Y., Zhao, Y., Li, F. M., & Li, T. J.-J. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747650 · Cited by 4
Toward Interpretable Graph Neural Networks via Concept Matching Model
Bui, T.-C., & Li, W.-S. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00106 · Cited by 4
The Facets and Consequences of Uncertainty in Human–AI Interaction
Magni, F., Yang, H. H. J., & Gong, Y. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197501061.013.22 · Cited by 4
Imperfections of XAI: Phenomena Influencing AI-Assisted Decision-Making
Spitzer, P., Morrison, K., Turri, V., Feng, M., Perer, A., & Kühl, N. (2025) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3750052 · Cited by 9
Building Appropriate Trust in AI: The Significance of Integrity-Centered Explanations
Mehrotra, S., Jorge, C. C., Jonker, C. M., & Tielman, M. L. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230121 · Cited by 6
A Game-Based Approach for Helping Designers Learn Machine Learning ConceptsPreprint
Myers, C. M., Xie, J., & Zhu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05605 · Cited by 4
Dual feature-based and example-based explanation methods
Konstantinov, A. V., Kozlov, B. N., Kirpichenko, S. R., Utkin, L. V., & Muliukha, V. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1506074 · Cited by 3
Leveraging Generative AI to Improve Comprehensibility in Social Recommender Systems
Ashaduzzaman, M., & Tsai, C.-H. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3708319.3733696 · Cited by 3
Learning Skills from Distance: A Solar Photovoltaic Site Survey from Students’ View
Masek, A., Nurtanto, M., Kholifah, N., Mutohhari, F., & Zainal, R. (2022) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2022.12.8.1677 · Cited by 3
Why is "Problems" Predictive of Positive Sentiment? A Case Study of Explaining Unintuitive Features in Sentiment Classification
Qu, J., Arguello, J., & Wang, Y. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658547 · Cited by 3
Supporting User Critiques of AI Systems via Training Dataset Explanations: Investigating Critique Properties and the Impact of Presentation Style
Anik, A. I., & Bunt, A. (2024) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00024 · Cited by 4
Designing Visual Explanations and Learner Controls to Engage Adolescents in AI-Supported Exercise Selection
Ooge, J., Vanneste, A., Szymanski, M., & Verbert, K. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706470 · Cited by 3
Adopting “blackbox” engineering advice: the influence of imperfect suggestions during AI-assisted decision-making with multiple objectives
Nandy, A., Herrera, D., & Goucher-Lambert, K. (2025) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060425000034 · Cited by 3
Explainable AI-Powered Autonomous Systems: Enhancing Trust and Transparency in Critical Applications
Aruna, S., Srilakshmi, K., Praveena, K., Veena, T., Prakash, B. P., & Jayapoorani, S. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.2494 · Cited by 2
Development and validation of the TechTra Scales: A tool to assess perceptions of and needs for technology transparency in UX research
Hein, I., Ullrich, D., & Diefenbach, S. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101025 · Cited by 2
Preliminary Scaling Characterization of TPCx-AI
Patel, H., Ufa, K., Nah, S., Raina, A., & Escobar, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-29576-8_6 · Cited by 2
(X)AI as a Teacher: Learning with Explainable Artificial Intelligence
Spitzer, P., Goutier, M., Kühl, N., & Satzger, G. (2024) · Mensch und Computer (MuC) · doi:10.1145/3670653.3677504 · Cited by 2
Erklärbare KI in der medizinischen Diagnose – Erfolge und Herausforderungen
Lucieri, A., Bajwa, M. N., Dengel, A. R., & Ahmed, S. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_35 · Cited by 2
Task Decomposition and Role Sharing for Real-time Human-AI Swarm Collaboration
Karakama, S., Matsunami, N., & Ito, M. (2021) · International Journal of Smart Computing and Artificial Intelligence · doi:10.52731/ijscai.v5.i1.637 · Cited by 1
Example-Based Conditioning for Text-to-Image Generative Models
Takada, A., Kawabe, W., & Sugano, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3486055 · Cited by 1
Integrating Explainable AI with Human-in-the-Loop Systems for Transparent Decision-Making in Autonomous Robots
Kumbhar, V. R., Sireesha, G., Jadhav, V. D., Barve, A., Sharma, R., & Soudagar, M. E. M. (2025) · International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) · doi:10.1109/icicnct66124.2025.11232580 · Cited by 1
HEPHA: A Mixed-Initiative Image Labeling Tool for Specialized Domains
Zhou, S., Li, B., Chen, X., Tu, Z., Wang, Y., Xiang, Y., & Zhang, T. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712072 · Cited by 1
Designing Effective Training Dataset Explanations: The Impact of Information Depth and Progressive Disclosure
Anik, A. I., & Bunt, A. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789087 · Cited by 2
Informing Users about Data Imputation: Exploring the Design Space for Dealing With Non-Responses
Bhattacharjee, A., Song, H., Wu, X., Tomlinson, J., Reza, M., Chowdhury, A. E., Deliu, N., Price, T., & Williams, J. J. (2023) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v11i1.27544 · Cited by 1
EXPLAINABLE AI AND ITS IMPORTANCE IN DECISION MAKING
Arora, H., Lamba, M., Kumar, L., & Kumar, S. (2023) · Journal of Nonlinear Analysis and Optimization Theory & Applications (JNAO) · doi:10.36893/jnao.2023.v14i1.0019-0027 · Cited by 1
Machine Learning Based Intercropping Recommender
Desai, S. S., & Wangi, K. (2023) · IEEE North Karnataka Subsection Flagship International Conference (NKCon) · doi:10.1109/nkcon59507.2023.10396335 · Cited by 1
Vers l’XAI sémantique – la troisième vague de l’intelligence artificielle explicable
Bollaert, M., & Coppin, G. (2024) · Ingénierie cognitique · doi:10.21494/iste.op.2024.1147 · Cited by 1
Slide to Explore 'What If': An Analysis of Explainable Interfaces
Meyer, L. S., & Zhu, J. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3677045.3685416 · Cited by 1
How Does Machine Advice Influence Design Choice? The Effect of Error on Design Decision Making
Nandy, A., & Goucher-Lambert, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-20418-0_47 · Cited by 2
Interacting with autonomous systems and intelligent algorithms – new theoretical considerations on the relation of understanding and trustPreprint
Krämer, N. C., Wischnewski, M., & Müller, E. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/h32ze · Cited by 2
A generic and adaptive approach to explainable AI in autonomic systems : the case of the smart home
Houzé, É. (2022) · theses.fr (ABES) · doi:10.70675/cd5e3060zd7a7z4c5czb731z7a240fad0c49 · Cited by 1
Misplaced trust in AI : the explanation paradox and the human-centric path. A characterisation of the cognitive challenges to appropriately trust algorithmic decisions and applications in the financial sector
Bertrand, A. (2024) · theses.fr (ABES) · doi:10.70675/216d3647z78bcz4c68z9ba9z2d0a029cf1b4 · Cited by 1
Considerations for applying logical reasoning to explain neural network outputs
Cau, F. M., Spano, L. D., & Tintarev, N. (2020) · Research Publications (Maastricht University)
CACTUS: Detecting and Resolving Conflicts in Objective FunctionsPreprint
Das, S., & Endert, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07805
Explanation using examples
Park, H., & Jung, J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85648-5.00009-8
Determining the Course Interests of Gifted Students using Machine Learning
Arapoglu, O., & Ciftci, R. T. (2022) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec56263.2022.9998191
Visual Analytics for Explainable and Trustworthy Machine Learning
Chatzimparmpas, A. (2023) · doi:10.15626/lud.482.2023
User tests & techniques for the post-hoc explanation of deep learning
Delaney, E. D., Kenny, E. M., Greene, D., & Keane, M. T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-396098-4.00019-3
Delivering AI Responsibly for Digital Equity: Three Sequential Field Experiments in Revenue-Based FinancingPreprint
Chung, S., Lee, C. H., & Zheng, E. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4548973

Authors: Cai, C. J., Jongejan, J., & Holbrook, J.
Affiliations: Google (United States) (US)
ORCID: Cai, Carrie J.
Holbrook, Jess
Venue: Proceedings of the 24th International Conference on Intelligent User Interfaces
Publisher: ACM
Published: 2019-03-17
Pages: 258-262
DOI: 10.1145/3301275.3302289
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
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
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 Graphics and Computer-Aided Design › 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.

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

The effects of example-based explanations in a machine learning interface

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks