On the Utility of Learning about Humans for Human-AI Coordination. - Micah Carroll; Rohin Shah; Mark K. Ho; Tom Griffiths 001; Sanjit A. Seshia; Pieter Abbeel; Anca D. Dragan (NeurIPS, 2019)

Evaluating the Robustness of Collaborative Agents. - Paul A. Knott; Micah Carroll; Sam Devlin; Kamil Ciosek; Katja Hofmann; Anca D. Dragan; Rohin Shah (AAMAS, 2021)

Estimating and Penalizing Preference Shift in Recommender Systems. - Micah Carroll; Dylan Hadfield-Menell; Stuart Russell 001; Anca D. Dragan (RecSys, 2021)

Estimating and Penalizing Induced Preference Shifts in Recommender Systems. - Micah D. Carroll; Anca D. Dragan; Stuart Russell 001; Dylan Hadfield-Menell (ICML, 2022)

Uni[MASK]: Unified Inference in Sequential Decision Problems. - Micah Carroll; Orr Paradise; Jessy Lin; Raluca Georgescu; Mingfei Sun 001; David Bignell; Stephanie Milani; Katja Hofmann; Matthew J. Hausknecht; Anca D. Dragan; Sam Devlin (NeurIPS, 2022)

Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers. - Micah Carroll; Jessy Lin; Orr Paradise; Raluca Georgescu; Mingfei Sun 001; David Bignell; Stephanie Milani; Katja Hofmann; Matthew J. Hausknecht; Anca D. Dragan; Sam Devlin (CoRR, 2022)

Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration. - Mesut Yang; Micah Carroll; Anca D. Dragan (CoRR, 2022)

UniMASK: Unified Inference in Sequential Decision Problems. - Micah Carroll; Orr Paradise; Jessy Lin; Raluca Georgescu; Mingfei Sun 001; David Bignell; Stephanie Milani; Katja Hofmann; Matthew J. Hausknecht; Anca D. Dragan; Sam Devlin (CoRR, 2022)

Time-Efficient Reward Learning via Visually Assisted Cluster Ranking. - David Zhang; Micah Carroll; Andreea Bobu; Anca D. Dragan (CoRR, 2022)

Characterizing Manipulation from AI Systems. - Micah Carroll; Alan Chan; Henry Ashton; David Krueger 001 (EAAMO, 2023)

Harms from Increasingly Agentic Algorithmic Systems. - Alan Chan; Rebecca Salganik; Alva Markelius; Chris Pang; Nitarshan Rajkumar; Dmitrii Krasheninnikov; Lauro Langosco; Zhonghao He; Yawen Duan; Micah Carroll; Michelle P. Lin; Alex Mayhew; et al. (FAccT, 2023)

Who Needs to Know? Minimal Knowledge for Optimal Coordination. - Niklas Lauffer; Ameesh Shah; Micah Carroll; Michael D. Dennis; Stuart Russell 001 (ICML, 2023)

Twitter's Algorithm: Amplifying Anger, Animosity, and Affective Polarization. - Smitha Milli; Micah Carroll; Sashrika Pandey; Yike Wang 002; Anca D. Dragan (CoRR, 2023)

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. - Stephen Casper; Xander Davies; Claudia Shi; Thomas Krendl Gilbert; Jérémy Scheurer; Javier Rando; Rachel Freedman; Tomasz Korbak; David Lindner; Pedro Freire; Tony Tong Wang; Samuel Marks; et al. (Trans. Mach. Learn. Res., 2023)

Defining Deception in Decision Making. - Marwa Abdulhai; Micah Carroll; Justin Svegliato; Anca D. Dragan; Sergey Levine (AAMAS, 2024)

AI Alignment with Changing and Influenceable Reward Functions. - Micah Carroll; Davis Foote; Anand Siththaranjan; Stuart Russell 001; Anca D. Dragan (ICML, 2024)

Beyond Preferences in AI Alignment. - Tan Zhi-Xuan; Micah Carroll; Matija Franklin; Hal Ashton (CoRR, 2024)

On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback. - Marcus Williams; Micah Carroll; Adhyyan Narang; Constantin Weisser; Brendan Murphy; Anca D. Dragan (ICLR, 2025)