Multiple relative pose graphs for robust cooperative mapping. - Been Kim; Michael Kaess; Luke Fletcher; John J. Leonard; Abraham Bachrach; Nicholas Roy; Seth J. Teller (ICRA, 2010)

Human-Inspired Techniques for Human-Machine Team Planning. - Julie A. Shah; Been Kim; Stefanos Nikolaidis (AAAI Fall Symposium: Human Control of Bioinspired Swarms, 2012)

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior. - Been Kim; Caleb M. Chacha; Julie A. Shah (AAAI, 2013)

Machine Learning for Meeting Analysis. - Been Kim; Cynthia Rudin (AAAI (Late-Breaking Developments), 2013)

Quantitative estimation of the strength of agreements in goal-oriented meetings. - Been Kim; Larry A. M. Bush; Julie Shah (CogSIMA, 2013)

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. - Been Kim; Cynthia Rudin; Julie A. Shah (NIPS, 2014)

Learning about meetings. - Been Kim; Cynthia Rudin (Data Min. Knowl. Discov., 2014)

Scalable and Interpretable Data Representation for High-Dimensional, Complex Data. - Been Kim; Kayur Patel; Afshin Rostamizadeh; Julie A. Shah (AAAI, 2015)

Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction. - Been Kim; Julie A. Shah; Finale Doshi-Velez (NIPS, 2015)

Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior. - Been Kim; Caleb M. Chacha; Julie A. Shah (J. Artif. Intell. Res., 2015)

Examples are not enough, learn to criticize! Criticism for Interpretability. - Been Kim; Oluwasanmi Koyejo; Rajiv Khanna (NIPS, 2016)

Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016). - Been Kim; Dmitry M. Malioutov; Kush R. Varshney (CoRR, 2016)

QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations. - Nan-Chen Chen; Been Kim (VAST, 2017)

A Roadmap for a Rigorous Science of Interpretability. - Finale Doshi-Velez; Been Kim (CoRR, 2017)

SmoothGrad: removing noise by adding noise. - Daniel Smilkov; Nikhil Thorat; Been Kim; Fernanda B. Viégas; Martin Wattenberg (CoRR, 2017)

Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017). - Been Kim; Dmitry M. Malioutov; Kush R. Varshney; Adrian Weller (CoRR, 2017)

The (Un)reliability of saliency methods. - Pieter-Jan Kindermans; Sara Hooker; Julius Adebayo; Maximilian Alber; Kristof T. Schütt; Sven Dähne; Dumitru Erhan; Been Kim (CoRR, 2017)

Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. - Julius Adebayo; Justin Gilmer; Ian J. Goodfellow; Been Kim (ICLR (Workshop), 2018)

Learning how to explain neural networks: PatternNet and PatternAttribution. - Pieter-Jan Kindermans; Kristof T. Schütt; Maximilian Alber; Klaus-Robert Müller; Dumitru Erhan; Been Kim; Sven Dähne (ICLR (Poster), 2018)

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). - Been Kim; Martin Wattenberg; Justin Gilmer; Carrie J. Cai; James Wexler; Fernanda B. Viégas; Rory Sayres (ICML, 2018)

Sanity Checks for Saliency Maps. - Julius Adebayo; Justin Gilmer; Michael Muelly; Ian J. Goodfellow; Moritz Hardt; Been Kim (NeurIPS, 2018)

To Trust Or Not To Trust A Classifier. - Heinrich Jiang; Been Kim; Melody Y. Guan; Maya R. Gupta (NeurIPS, 2018)

Human-in-the-Loop Interpretability Prior. - Isaac Lage; Andrew Slavin Ross; Samuel J. Gershman; Been Kim; Finale Doshi-Velez (NeurIPS, 2018)

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. - Menaka Narayanan; Emily Chen; Jeffrey He; Been Kim; Sam Gershman; Finale Doshi-Velez (CoRR, 2018)

To Trust Or Not To Trust A Classifier. - Heinrich Jiang; Been Kim; Maya R. Gupta (CoRR, 2018)

xGEMs: Generating Examplars to Explain Black-Box Models. - Shalmali Joshi; Oluwasanmi Koyejo; Been Kim; Joydeep Ghosh (CoRR, 2018)

Evaluating Feature Importance Estimates. - Sara Hooker; Dumitru Erhan; Pieter-Jan Kindermans; Been Kim (CoRR, 2018)

Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018). - Been Kim; Kush R. Varshney; Adrian Weller (CoRR, 2018)

Interpreting Black Box Predictions using Fisher Kernels. - Rajiv Khanna; Been Kim; Joydeep Ghosh; Sanmi Koyejo (AISTATS, 2019)

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. - Carrie J. Cai; Emily Reif; Narayan Hegde; Jason D. Hipp; Been Kim; Daniel Smilkov; Martin Wattenberg; Fernanda B. Viégas; Gregory S. Corrado; Martin C. Stumpe; Michael Terry (CHI, 2019)

Human Evaluation of Models Built for Interpretability. - Isaac Lage; Emily Chen; Jeffrey He; Menaka Narayanan; Been Kim; Samuel J. Gershman; Finale Doshi-Velez (HCOMP, 2019)

Towards Automatic Concept-based Explanations. - Amirata Ghorbani; James Wexler; James Y. Zou; Been Kim (NeurIPS, 2019)

A Benchmark for Interpretability Methods in Deep Neural Networks. - Sara Hooker; Dumitru Erhan; Pieter-Jan Kindermans; Been Kim (NeurIPS, 2019)

Visualizing and Measuring the Geometry of BERT. - Emily Reif; Ann Yuan; Martin Wattenberg; Fernanda B. Viégas; Andy Coenen; Adam Pearce; Been Kim (NeurIPS, 2019)

An Evaluation of the Human-Interpretability of Explanation. - Isaac Lage; Emily Chen; Jeffrey He; Menaka Narayanan; Been Kim; Sam Gershman; Finale Doshi-Velez (CoRR, 2019)

Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks. - Amirata Ghorbani; James Wexler; Been Kim (CoRR, 2019)

Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure. - Been Kim; Emily Reif; Martin Wattenberg; Samy Bengio (CoRR, 2019)

Explaining Classifiers with Causal Concept Effect (CaCE). - Yash Goyal; Uri Shalit; Been Kim (CoRR, 2019)

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems. - Shalmali Joshi; Oluwasanmi Koyejo; Warut Vijitbenjaronk; Been Kim; Joydeep Ghosh (CoRR, 2019)

BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth. - Mengjiao Yang; Been Kim (CoRR, 2019)

On Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Pradeep Ravikumar; Tomas Pfister (CoRR, 2019)

The (Un)reliability of Saliency Methods. - Pieter-Jan Kindermans; Sara Hooker; Julius Adebayo; Maximilian Alber; Kristof T. Schütt; Sven Dähne; Dumitru Erhan; Been Kim (Explainable AI, 2019)

Concept Bottleneck Models. - Pang Wei Koh; Thao Nguyen; Yew Siang Tang; Stephen Mussmann; Emma Pierson; Been Kim; Percy Liang (ICML, 2020)

Debugging Tests for Model Explanations. - Julius Adebayo; Michael Muelly; Ilaria Liccardi; Been Kim (NeurIPS, 2020)

On Completeness-aware Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Tomas Pfister; Pradeep Ravikumar (NeurIPS, 2020)

Machine Learning Techniques for Accountability. - Been Kim; Finale Doshi-Velez (AI Mag., 2021)

Best of both worlds: local and global explanations with human-understandable concepts. - Jessica Schrouff; Sebastien Baur; Shaobo Hou; Diana Mincu; Eric Loreaux; Ralph Blanes; James Wexler; Alan Karthikesalingam; Been Kim (CoRR, 2021)

Acquisition of Chess Knowledge in AlphaZero. - Thomas McGrath; Andrei Kapishnikov; Nenad Tomasev; Adam Pearce; Demis Hassabis; Been Kim; Ulrich Paquet; Vladimir Kramnik (CoRR, 2021)

Analyzing a Caching Model. - Leon Sixt; Evan Zheran Liu; Marie Pellat; James Wexler; Milad Hashemi; Been Kim; Martin Maas 001 (CoRR, 2021)

Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. - Xiao Bai 001; Xiang Wang 014; Xianglong Liu 001; Qiang Liu 001; Jingkuan Song; Nicu Sebe; Been Kim (Pattern Recognit., 2021)

Human-Centered Concept Explanations for Neural Networks. - Chih-Kuan Yeh; Been Kim; Pradeep Ravikumar (Neuro-Symbolic Artificial Intelligence, 2021)

Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. - Julius Adebayo; Michael Muelly; Harold Abelson; Been Kim (ICLR, 2022)

DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. - Asma Ghandeharioun; Been Kim; Chun-Liang Li; Brendan Jou; Brian Eoff; Rosalind W. Picard (ICLR, 2022)

Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. - Shayegan Omidshafiei; Andrei Kapishnikov; Yannick Assogba; Lucas Dixon; Been Kim (NeurIPS, 2022)

Human-Centered Concept Explanations for Neural Networks. - Chih-Kuan Yeh; Been Kim; Pradeep Ravikumar (CoRR, 2022)

Impossibility Theorems for Feature Attribution. - Blair L. Bilodeau; Natasha Jaques; Pang Wei Koh; Been Kim (CoRR, 2022)

On the Relationship Between Explanation and Prediction: A Causal View. - Amir-Hossein Karimi; Krikamol Muandet; Simon Kornblith; Bernhard Schölkopf; Been Kim (ICML, 2023)

Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems. - Devleena Das; Been Kim; Sonia Chernova (IUI, 2023)

State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding. - Devleena Das; Sonia Chernova; Been Kim (NeurIPS, 2023)

Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. - Peter Hase; Mohit Bansal; Been Kim; Asma Ghandeharioun (NeurIPS, 2023)

Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. - Zi Wang; Alexander Ku; Jason Baldridge; Tom Griffiths 001; Been Kim (NeurIPS, 2023)

Model evaluation for extreme risks. - Toby Shevlane; Sebastian Farquhar; Ben Garfinkel; Mary Phuong; Jess Whittlestone; Jade Leung; Daniel Kokotajlo; Nahema Marchal; Markus Anderljung; Noam Kolt; Lewis Ho; Divya Siddarth; et al. (CoRR, 2023)

Getting aligned on representational alignment. - Ilia Sucholutsky; Lukas Muttenthaler; Adrian Weller; Andi Peng; Andreea Bobu; Been Kim; Bradley C. Love; Erin Grant; Jascha Achterberg; Joshua B. Tenenbaum; Katherine M. Collins; Katherine L. Hermann; et al. (CoRR, 2023)

Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero. - Lisa Schut; Nenad Tomasev; Tom McGrath; Demis Hassabis; Ulrich Paquet; Been Kim (CoRR, 2023)

TabCBM: Concept-based Interpretable Neural Networks for Tabular Data. - Mateo Espinosa Zarlenga; Zohreh Shams; Michael E. Nelson; Been Kim; Mateja Jamnik (Trans. Mach. Learn. Res., 2023)

Don't trust your eyes: on the (un)reliability of feature visualizations. - Robert Geirhos; Roland S. Zimmermann; Blair L. Bilodeau; Wieland Brendel; Been Kim (ICML, 2024)

Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty. - Meera Hahn; Wenjun Zeng; Nithish Kannen; Rich Galt; Kartikeya Badola; Been Kim; Zi Wang (CoRR, 2024)

How new data permeates LLM knowledge and how to dilute it. - Chen Sun; Renat Aksitov; Andrey Zhmoginov; Nolan Andrew Miller; Max Vladymyrov; Ulrich Rueckert; Been Kim; Mark Sandler 002 (ICLR, 2025)

We Can't Understand AI Using our Existing Vocabulary. - John Hewitt; Robert Geirhos; Been Kim (CoRR, 2025)

QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks? - Belinda Z. Li; Been Kim; Zi Wang (CoRR, 2025)

Because we have LLMs, we Can and Should Pursue Agentic Interpretability. - Been Kim; John Hewitt; Neel Nanda; Noah Fiedel; Oyvind Tafjord (CoRR, 2025)

Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models. - Lauren Hyoseo Yoon; Yisong Yue; Been Kim (CoRR, 2025)