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To explain or not to explain: the effects of personal characteristics when explaining music recommendations

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Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach

Yang, S., Korayem, M., AlJadda, K., Grainger, T., & Natarajan, S. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.08.017 · Cited by 144

I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI

Chromik, M., Eiband, M., Buchner, F., Krüger, A., & Butz, A. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450644 · Cited by 144 · cited 12×

How model accuracy and explanation fidelity influence user trustPreprint

Papenmeier, A., Englebienne, G., & Seifert, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12652 · Cited by 139

Answering Questions about Charts and Generating Visual Explanations

Kim, D. H., Hoque, E., & Agrawala, M. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376467 · Cited by 133

Visual, textual or hybrid: the effect of user expertise on different explanations

Szymanski, M., Millecamp, M., & Verbert, K. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450662 · Cited by 131

Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation

Yang, Q., Banović, N., & Zimmerman, J. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173704 · Cited by 130

Explainable Recommendation through Attentive Multi-View Learning

Gao, J., Wang, X., Wang, Y., & Xie, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013622 · Cited by 127

Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning

Gajos, K. Z., & Mamykina, L. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511138 · Cited by 127

"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans

Lai, V., Liu, H., & Tan, C. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376873 · Cited by 123

Effects of Semantic Segmentation Visualization on Trust, Situation Awareness, and Cognitive Load in Highly Automated Vehicles

Colley, M., Eder, B., Rixen, J. O., & Rukzio, E. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445351 · Cited by 121

Reframing Human-AI Collaboration for Generating Free-Text Explanations

Wiegreffe, S., Hessel, J., Swayamdipta, S., Riedl, M. O., & Choi, Y. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.47 · Cited by 121

An empirical study on the perceived fairness of realistic, imperfect machine learning models

Harrison, G., Hanson, J., Jacinto, C., Ramirez, J., & Ur, B. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372831 · Cited by 120

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Liao, Q. V., & Varshney, K. R. (2021) · arXiv · doi:10.48550/arxiv.2110.10790 · Cited by 120

I can do better than your AI: expertise and explanations

Schaffer, J., O'Donovan, J., Michaelis, J., Raglin, A., & Höllerer, T. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302308 · Cited by 119

Who needs to know what, when?: Broadening the Explainable AI (XAI) Design Space by Looking at Explanations Across the AI Lifecycle

Dhanorkar, S., Wolf, C. T., Qian, K., Xu, A., Popa, L., & Li, Y. (2021) · Designing Interactive Systems Conference 2021 · doi:10.1145/3461778.3462131 · Cited by 119

Progressive disclosure

Springer, A., & Whittaker, S. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302322 · Cited by 115 · cited 4×

No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML

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

Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems

Nourani, M., Roy, C., Block, J. E., Honeycutt, D. R., Rahman, T., Ragan, E., & Gogate, V. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450639 · Cited by 107 · cited 11×

Does Explainable Artificial Intelligence Improve Human Decision-Making?

Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y., & Kantarcioglu, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16819 · Cited by 106

Enabling robots to communicate their objectives

Huang, S. H., Held, D., Abbeel, P., & Dragan, A. D. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9771-0 · Cited by 103

Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency

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 · cited 11×

It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy

Bell, A., Solano-Kamaiko, I., Nov, O., & Stoyanovich, J. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533090 · Cited by 103

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

Establishing Appropriate Trust via Critical States

Huang, S. H., Bhatia, K., Abbeel, P., & Dragan, A. D. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593649 · Cited by 94

What can AI do for me?: evaluating machine learning interpretations in cooperative play

Feng, S., & Boyd-Graber, J. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302265 · Cited by 92

Measuring Cognitive Load: Are There More Valid Alternatives to Likert Rating Scales?

Ouwehand, K., Kroef, A. V. D., Wong, J., & Paas, F. (2021) · Frontiers in Education · doi:10.3389/feduc.2021.702616 · Cited by 90

Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers

Tsai, C.-H., You, Y., Gui, X., Kou, Y., & Carroll, J. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445101 · Cited by 86

ExplAIn Yourself! Transparency for Positive UX in Autonomous Driving

Schneider, T., Hois, J., Rosenstein, A., Ghellal, S., Theofanou-Fülbier, D., & Gerlicher, A. R. S. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3446647 · Cited by 81

Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

Grgić-Hlača, N., Redmiles, E. M., Gummadi, K. P., & Weller, A. (2018) · arXiv · doi:10.48550/arxiv.1802.09548 · Cited by 75

Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

Lai, V., Chen, C., Liao, Q. V., Smith-Renner, A., & Tan, C. (2021) · arXiv · doi:10.48550/arxiv.2112.11471 · Cited by 75

Crowdsourcing Perceptions of Fair Predictors for Machine Learning

van Berkel, N., Gonçalves, J., Hettiachchi, D., Wijenayake, S., Kelly, R., & Kostakos, V. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359130 · Cited by 74

Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach

Bang, S., Xie, P., Lee, H., Wu, W., & Xing, E. P. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17358 · Cited by 73

HIVE: Evaluating the Human Interpretability of Visual Explanations

Kim, S. S. Y., Meister, N., Ramaswamy, V. V., Fong, R., & Russakovsky, O. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19775-8_17 · Cited by 72

Do explanations make VQA models more predictable to a human?

Chandrasekaran, A., Prabhu, V., Yadav, D., Chattopadhyay, P., & Parikh, D. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1128 · Cited by 69

What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Ferreira, J. J., & Monteiro, M. S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-49760-6_4 · Cited by 68

Contextualization and Exploration of Local Feature Importance Explanations to Improve Understanding and Satisfaction of Non-Expert Users

Bove, C., Aigrain, J., Lesot, M., Tijus, C., & Detyniecki, M. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511139 · Cited by 68

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

Explaining Recommendations in E-Learning: Effects on Adolescents' Trust

Ooge, J., Kato, S., & Verbert, K. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511140 · Cited by 63

Explaining recommendations in an interactive hybrid social recommender

Tsai, C.-H., & Brusilovsky, P. L. (2019) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3301275.3302318 · Cited by 60

User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering

Liao, M., Sundar, S. S., & Walther, J. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501936 · Cited by 59

The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images

Dominguez, V., Messina, P., Donoso-Guzmán, I., & Parra, D. (2019) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3301275.3302274 · Cited by 55

A deep learning model for behavioural credit scoring in banks

Alaraj, M., Abbod, M., Majdalawieh, M., & Jum’a, L. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06695-z · Cited by 54

Regularizing Black-box Models for Improved InterpretabilityPreprint

Plumb, G., Al-Shedivat, M., Cabrera, Á. A., Perer, A., Xing, E. P., & Talwalkar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06787 · Cited by 53

Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules

Guo, L., Daly, E., Alkan, Ö., Mattetti, M., Cornec, O., & Knijnenburg, B. P. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511111 · Cited by 53

Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human PerceptionsPreprint

Ehsan, U., Tambwekar, P., Chan, L., Harrison, B. E., & Riedl, M. O. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.03729 · Cited by 52

Leveraging rationales to improve human task performance

Das, D., & Chernova, S. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377512 · Cited by 52

Exploring Computational User Models for Agent Policy Summarization

Lage, I., Lifschitz, D., Doshi‐Velez, F., & Amir, O. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/194 · Cited by 51

Interpretable machine learning

Molnar, C. (2020) · doi:10.58248/pn633 · Cited by 51

Explaining the News Feed Algorithm

Cotter, K., Cho, J., & Rader, E. J. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053114 · Cited by 49

Foundations for an Empirically Determined Scale of Trust in Automated Systems

Jian, J.-Y., Bisantz, A. M., Drury, C. G., et al. (1998) · doi:10.21236/ada388787 · Cited by 49

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Antorán, J., Bhatt, U., Adel, T., Weller, A., & Hernández-Lobato, J. M. (2021) · Apollo - University of Cambridge Repository · doi:10.17863/cam.69929 · Cited by 49

Beyond the Ranked List

Tsai, C.-H., & Brusilovsky, P. L. (2018) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3172944.3172959 · Cited by 47

Machine learning in medical imaging

Suzuki, K., Zhou, L., & Wang, Q. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.020 · Cited by 42

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable RecoursesPreprint

Rawal, K., & Lakkaraju, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.07165 · Cited by 42

Why Am I Not Seeing It? Understanding Users’ Needs for Counterfactual Explanations in Everyday Recommendations

Shang, R., Feng, K. J. K., & Shah, C. S. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533189 · Cited by 42

Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User StudyPreprint

Alqaraawi, A., Schuessler, M., Weiß, P., Costanza, E., & Bianchi‐Berthouze, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00772 · Cited by 41

Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations

Arora, S., Pruthi, D., Sadeh, N., Cohen, W. W., Lipton, Z. C., & Neubig, G. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i5.20464 · Cited by 41

Evaluating the Interpretability of Generative Models by Interactive Reconstruction

Ross, A., Chen, N., Hang, E. Z., Glassman, E. L., & Doshi-Velez, F. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445296 · Cited by 40

Improving understandability of feature contributions in model-agnostic explainable AI tools

Hadash, S., Willemsen, M. C., Snijders, C. C. P., & IJsselsteijn, W. A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517650 · Cited by 40

A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI.

Chromik, M., & Schuessler, M. (2020) · Cited by 39

Getting a CLUE: A Method for Explaining Uncertainty EstimatesPreprint

Antorán, J., Bhatt, U., Adel, T., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06848 · Cited by 38

What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative PlayPreprint

Feng, S., & Boyd-Graber, J. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09648 · Cited by 37

Mitigating belief projection in explainable artificial intelligence via Bayesian teaching

Yang, S. C., Vong, W. K., Sojitra, R. B., Folke, T., & Shafto, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-89267-4 · Cited by 37

How Should AI Systems Talk to Users when Collecting their Personal Information? Effects of Role Framing and Self-Referencing on Human-AI Interaction

Liao, M., & Sundar, S. S. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445415 · Cited by 36

Human Interpretation of Saliency-based Explanation Over Text

Schuff, H., Jacovi, A., Adel, H., Goldberg, Y., & Vu, N. T. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533127 · Cited by 36

The effectiveness of feature attribution methods and its correlation with automatic evaluation scoresPreprint

Nguyen, G., Kim, D., & Nguyen, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.14944 · Cited by 35

How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels

Shen, H., & Huang, T.-H. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7477 · Cited by 32

Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

Kuhl, U., Artelt, A., & Hammer, B. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3534630 · Cited by 32

A Consistent and Efficient Evaluation Strategy for Attribution MethodsPreprint

Rong, Y., Leemann, T., Borisov, V., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.00449 · Cited by 31

Exploring the Role of Local and Global Explanations in Recommender Systems

Radensky, M., Downey, D., Lo, K., Popović, Z., & Weld, D. S. (2022) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3491101.3519795 · Cited by 29

Do Humans Prefer Debiased AI Algorithms? A Case Study in Career Recommendation

Wang, C., Wang, K., Bian, A., Islam, R., Keya, K. N., Foulds, J. R., & Pan, S. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511108 · Cited by 29

Explaining recommendations by means of aspect-based transparent memories

Donkers, T., Kleemann, T., & Ziegler, J. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377520 · Cited by 27

Improving User Confidence in Concept Maps

Bras, P. L., Robb, D. A., Methven, T. S., Padilla, S., & Chantler, M. J. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173978 · Cited by 26

Towards Interpretable Natural Language Understanding with Explanations as Latent VariablesPreprint

Zhou, W., Hu, J., Zhang, H., Liang, X., Sun, M., Xiong, C., & Tang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.05268 · Cited by 26

Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs

Suresh, H., Lewis, K. M., Guttag, J. V., & Satyanarayan, A. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511160 · Cited by 26

How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?

Balayn, A., Rikalo, N., Lofi, C., Yang, J., & Bozzon, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517474 · Cited by 26

Use-Case-Grounded Simulations for Explanation EvaluationPreprint

Chen, V., Johnson, N., Topin, N., Plumb, G., & Talwalkar, A. (2022) · Neural Information Processing Systems · doi:10.48550/arxiv.2206.02256 · Cited by 26

FontMatcher

Choi, S., Aizawa, K., & Sebe, N. (2018) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3172944.3173001 · Cited by 25

Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems

Schoeffer, J., & Kuehl, N. (2021) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3462204.3481742 · Cited by 25

Parallel embeddings

Arendt, D., Nur, N., Huang, Z., et al. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377514 · Cited by 24

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

Rong, Y., Han, C., Hellert, C., Loyal, A., & Kasneci, E. (2021) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2021.3050883 · Cited by 24

Model Agnostic Multilevel ExplanationsPreprint

Ramamurthy, K. N., Vinzamuri, B., Zhang, Y., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06005 · Cited by 21

Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation

Wang, Y., Venkatesh, P., & Lim, B. Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517551 · Cited by 21

Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning

Zhang, W., Dimiccoli, M., & Lim, B. Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517522 · Cited by 21

XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering

Rebanal, J., Combitsis, J., Tang, Y., & Chen, X. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450676 · Cited by 20

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

Kuhl, U., Artelt, A., & Hammer, B. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1087929 · Cited by 20

Are Attention Check Questions a Threat to Scale Validity?Preprint

Kung, F. Y. H., Kwok, N., & Brown, D. (2017) · doi:10.31234/osf.io/9vrdn · Cited by 17

Digging into user control

Smith, A., Kumar, V. R., Boyd-Graber, J. L., Seppi, K. D., & Findlater, L. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377491 · Cited by 14

User Trust on an Explainable AI-based Medical Diagnosis Support SystemPreprint

Rong, Y., Castner, N., Bozkir, E., & Kasneci, E. (2022) · arXiv.org · doi:10.48550/arxiv.2204.12230 · Cited by 13

Perception of Fairness in Group Music Recommender Systems

Htun, N. N., Lecluse, E., & Verbert, K. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450642 · Cited by 12

What large language models know and what people think they know

Steyvers, M., Tejeda, H., Kumar, A., Belem, C., Karny, S., Hu, X., Mayer, L. W., & Smyth, P. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00976-7 · Cited by 209

On opportunities and challenges of large multimodal foundation models in education

Küchemann, S., Avila, K. E., Dinc, Y., Hortmann, C., Revenga, N., Ruf, V., Stausberg, N., Steinert, S., Fischer, F., Fischer, M., et al. (2025) · npj Science of Learning · doi:10.1038/s41539-025-00301-w · Cited by 47

Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making

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

Building Appropriate Mental Models: What Users Know and Want to Know about an Agentic AI Chatbot

Brachman, M., Kunde, S., Miller, S., Fucs, A., Dempsey, S., Jabbour, J., & Geyer, W. (2025) · Proceedings of the 30th International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712071 · Cited by 18

Survey of Computerized Adaptive Testing: A Machine Learning Perspective

Zhuang, Y., Liu, Q., Bi, H., Huang, Z., Huang, W., Li, J., Yu, J., Liu, Z., Hu, Z., Hong, Y., et al. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3672850 · Cited by 9

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

Padmanabhan, M., Yuan, P., Chada, G., & Nguyen, H. V. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8071050 · Cited by 77

Breakdown of Machine Learning Algorithms

Meena, M., & Sehrawat, R. (2022) · Recent Trends in Artificial Intelligence & it s Applications · doi:10.46610/rtaia.2022.v01i03.005 · Cited by 1

Interpretable Machine Learning Assessment

Han, H., Wu, Y., Wang, J., & Han, A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2023.126891 · Cited by 31

Application of Machine Learning in Animal Disease Analysis and Prediction

Zhang, S., Su, Q., & Chen, Q. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200728195613 · Cited by 29

Machine Learning Techniques for the Management of Diseases: A Paper Review

Tim, N. K., Kamla, V. C., & Tagne, E. F. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-56396-6_23