| Towards a rigorous science of interpretable machine learning F Doshi-Velez, B Kim arXiv preprint arXiv:1702.08608, 2017 | 8260 | 2017 |
| Smoothgrad: removing noise by adding noise D Smilkov, N Thorat, B Kim, F Viégas, M Wattenberg arXiv preprint arXiv:1706.03825, 2017 | 3191 | 2017 |
| Sanity checks for saliency maps J Adebayo, J Gilmer, M Muelly, I Goodfellow, M Hardt, B Kim Advances in Neural Information Processing Systems, 9505-9515, 2018 | 3072 | 2018 |
| Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) RS Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler ... https://arxiv.org/abs/1711.11279, https://arxiv.org/abs/1711.11279, 2018 | 2937* | 2018 |
| Concept bottleneck models PW Koh, T Nguyen, YS Tang, S Mussmann, E Pierson, B Kim, P Liang International Conference on Machine Learning, 5338-5348, 2020 | 1424 | 2020 |
| Examples are not enough, learn to criticize! criticism for interpretability B Kim, R Khanna, OO Koyejo Advances in Neural Information Processing Systems, 2280-2288, 2016 | 1352 | 2016 |
| Towards automatic concept-based explanations A Ghorbani, J Wexler, JY Zou, B Kim Advances in Neural Information Processing Systems, 9277-9286, 2019 | 1055 | 2019 |
| A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim Advances in Neural Information Processing Systems, 9737-9748, 2019 | 1021 | 2019 |
| The (un) reliability of saliency methods PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267-280, 2019 | 977 | 2019 |
| To trust or not to trust a classifier H Jiang, B Kim, M Guan, M Gupta Advances in neural information processing systems, 5541-5552, 2018 | 651 | 2018 |
| Human-centered tools for coping with imperfect algorithms during medical decision-making CJ Cai, E Reif, N Hegde, J Hipp, B Kim, D Smilkov, M Wattenberg, ... Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems …, 2019 | 611 | 2019 |
| Visualizing and Measuring the Geometry of BERT E Reif, A Yuan, M Wattenberg, FB Viegas, A Coenen, A Pearce, B Kim Advances in Neural Information Processing Systems, 8594-8603, 2019 | 557 | 2019 |
| Learning how to explain neural networks: PatternNet and PatternAttribution PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne arXiv preprint arXiv:1705.05598, 2017 | 485 | 2017 |
| Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification B Kim, C Rudin, J Shah Neural Information Processing Systems (NIPS), 2014 | 453 | 2014 |
| On Completeness-aware Concept-Based Explanations in Deep Neural Networks CK Yeh, B Kim, S Arik, CL Li, T Pfister, P Ravikumar Advances in Neural Information Processing Systems 33, 2020 | 439 | 2020 |
| Considerations for evaluation and generalization in interpretable machine learning F Doshi-Velez, B Kim Explainable and Interpretable Models in Computer Vision and Machine Learning …, 2018 | 301 | 2018 |
| An Evaluation of the Human-Interpretability of Explanation I Lage, E Chen, J He, M Narayanan, B Kim, S Gershman, F Doshi-Velez arXiv preprint arXiv:1902.00006, 2019 | 289 | 2019 |
| How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation M Narayanan, E Chen, J He, B Kim, S Gershman, F Doshi-Velez arXiv preprint arXiv:1802.00682, 2018 | 282 | 2018 |
| Multiple relative pose graphs for robust cooperative mapping B Kim, M Kaess, L Fletcher, J Leonard, A Bachrach, N Roy, S Teller Robotics and Automation (ICRA), 2010 IEEE International Conference on, 3185-3192, 2010 | 278 | 2010 |
| Debugging Tests for Model Explanations J Adebayo, M Muelly, I Liccardi, B Kim Neurips 2020, 2020 | 255 | 2020 |
Finale Doshi-VelezProfessor, HarvardVerified email at seas.harvard.edu
