Kim, Been

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 3,121

SmoothGrad: removing noise by adding noise

Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 1,467

Sanity Checks for Saliency Maps

Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Visual Attention and Saliency Detection

Cited by 1,294

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

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 1,240

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

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 405

Towards Automatic Concept-based Explanations

Ghorbani, A., Wexler, J., Zou, J., & Kim, B. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 378

Concept Bottleneck Models

Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., & Liang, P. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Machine Learning in Healthcare

Cited by 338

Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

Bai, X., Wang, X., Liu, X., Liu, Q., Song, J., Sebe, N., & Kim, B. (2021) · Pattern Recognition

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 313

Considerations for Evaluation and Generalization in Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2018) · The Springer Series on Challenges in Machine Learning

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 176

Acquisition of chess knowledge in AlphaZero

McGrath, T., Kapishnikov, A., Tomašev, N., Pearce, A., Wattenberg, M., Hassabis, D., Kim, B., Paquet, U., & Kramnik, V. (2022) · Proceedings of the National Academy of Sciences

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 166

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation

Narayanan, M., Chen, E., He, J., Kim, B., Gershman, S., & Doshi-Velez, F. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 153

An Evaluation of the Human-Interpretability of Explanation

Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S., & Doshi-Velez, F. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Data Visualization and Analytics

Cited by 125

Debugging Tests for Model Explanations

Adebayo, J., Muelly, M., Liccardi, I., & Kim, B. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 88

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Model evaluation for extreme risks

Shevlane, T., Farquhar, S., Garfinkel, B., Phuong, M., Whittlestone, J., Leung, J., Kokotajlo, D., Marchal, N., Anderljung, M., Kolt, N., et al. (2023) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 66

Getting aligned on representational alignment

Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Cueva, C. J., Grant, E., Groen, I., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningExplainable Artificial Intelligence (XAI)

Cited by 36

Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models

Hase, P., Bansal, M., Kim, B., & Ghandeharioun, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 33

Machine Learning Techniques for Accountability

Kim, B., & Doshi‐Velez, F. (2021) · AI Magazine

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 18

Video models are zero-shot learners and reasoners

Wiedemer, T., Li, Y., Vicol, P., Gu, S. S., Matarese, N., Swersky, K., Kim, B., Jaini, P., & Geirhos, R. (2025) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 2

Because we have LLMs, we Can and Should Pursue Agentic Interpretability

Kim, B., Hewitt, J., Nanda, N., Fiedel, N., & Tafjord, O. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 1