Headshot of Andreas Haupt

Andreas Haupt

AI Institute Fellow-in-Residence, Schmidt Sciences

Digital Fellow, Stanford Digital Economy Lab

Technology and Human Rights Fellow, Harvard Kennedy School

Andreas Haupt is an AI Institute Fellow-in-Residence at Schmidt Sciences, a Digital Fellow at the Stanford Digital Economy Lab, and a Technology and Human Rights Fellow at the Harvard Kennedy School. He studies how algorithmic systems represent diverse human preferences, including questions of privacy, competition, and consumer protection. He develops and applies methods of microeconomic theory, structural econometrics, and reinforcement learning to these domains; his work has been published in AI venues such as NeurIPS and ICML, in computer science venues such as ACM EC, ACM RecSys, and ACM FAccT, and in the American Economic Review. He earned a Ph.D. in Engineering-Economic Systems from MIT in February 2025 with a committee evenly split between Economics and Computer Science. Prior to that, he completed two master’s degrees at the University of Bonn—first in Mathematics (2017) and then in Economics (2018), with distinction. He has worked on competition enforcement for the European Commission’s Directorate-General for Competition and the U.S. Federal Trade Commission, and taught high school mathematics and computer science in Germany before his Ph.D. He remains committed to education and scholarship, most recently as a co-author of a textbook on Machine Learning from Human Preferences, forthcoming with Princeton University Press.

280-character bio Andreas Haupt is an AI Institute Fellow-in-Residence at Schmidt Sciences and a fellow at Stanford's Digital Economy Lab and the Harvard Kennedy School. He studies how algorithmic systems represent diverse human preferences, publishing in NeurIPS, ICML, ACM EC, and the AER.
Tagline Use AI to Learn about People's Preferences
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Publications

A more complete list of publications can be found on Google Scholar. ‡ indicates equal contribution or alphabetic author listing. Only published work is shown by default; select another type, or All types, to see the rest.

Welfare, Improvability, and Variance: A Principal-Agent Approach to Optimal Benchmark Item Aggregation

A. Haupt‡, J. Hartenstein‡, A. Reuel, M. Kochenderfer, S. Koyejo

Advances in Neural Information Processing Systems (Evaluations and Datasets Track), 2026.

Published AI Evaluation Aggregation Market Design PDF HTML

Reward Bias Substitution: Single-Axis Bias Mitigations Redirect Optimization Pressure

M. Lamparth, D. Fein, A. Haupt, M. Hussing, M.J. Kochenderfer

Advances in Neural Information Processing Systems, 2026.

Published Preferences PDF

Position: AI Development Should Prioritize Cognitive Security

B. El, S. Su, A. Pappu, P. Yin, J. Heng, E. Heng, R.Z. Wang, A. Haupt‡, J. Zou‡

Advances in Neural Information Processing Systems (Position Paper), 2026.

Published Preferences PDF

Contextually Private Mechanisms

A. Haupt‡, Z. Hitzig‡

American Economic Review (Lead Article), 2026.

Published Privacy Market Design PDF HTML

Machine Learning from Human Preferences

S. Truong‡, A. Haupt‡, S. Koyejo‡

Princeton University Press (forthcoming)

Published Preferences HTML

Don't Walk the Line: Boundary Guidance for Filtered Generation

S. Ball, A. Haupt

International Conference on Machine Learning, 2026.

Published Multi-Agent PDF

Bruno

A. Haupt‡, P. Ranganathan‡

ICML Workshop on AI for Science (Best AI Scientist Award), 2026.

Published Work HTML VIDEO

The Collapse of Heterogeneity in Silicon Philosophers

Y. Shi, A. Haupt

ACM Conference on Fairness, Accountability, and Transparency, 2026.

Published AI Evaluation Preferences PDF CODE

Discussion of "The ICML 2023 Ranking Experiment" by Su et al.

A. Haupt, S. Koyejo

Journal of the American Statistical Association 121 (554), p. 853-854, 2026.

Published AI Evaluation Aggregation

Position: ML Conferences Should Establish a Refutations and Critiques Track

R. Schaeffer, J. Kazdan, Y. Denisov-Blanch, B. Miranda, M. Gerstgrasser, S. Zhang, A. Haupt, I. Gupta, E. Obbad, J. Dodge, et al.

Advances in Neural Information Processing Systems (Position Paper), 2025.

Published AI Evaluation PDF

Scaling Human Judgment in Community Notes with LLMs

H. Li, S. De, M. Revel, A. Haupt, B. Miller, K. Coleman, J. Baxter, M. Saveski, M.A. Bakker

Journal of Online Trust and Safety 3 (1), 2025.

Published AI Evaluation Aggregation Platforms PDF

AI should not be an imitation game: Centaur evaluations

A. Haupt, E. Brynjolfsson

International Conference on Machine Learning (Position Paper), 2025.

Published AI Evaluation Work PDF

Convex Markov Games: A Framework for Creativity, Imitation, Fairness, and Safety in Multiagent Learning

I. Gemp, A. Haupt, L. Marris, S. Liu, G. Piliouras

International Conference on Machine Learning, 2025.

Published Multi-Agent PDF

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

B. Zhang, G. Farina, I. Anagnostides, F. Cacciamani, S. McAleer, A. Haupt, A. Celli, N. Gatti, V. Conitzer, T. Sandholm

Advances in Neural Information Processing Systems, 2024.

Published Multi-Agent Market Design PDF

Risk Preferences of Learning Algorithms

A. Haupt‡, A. Narayanan‡

Games and Economic Behavior 148, p. 415-426, 2024.

Published Multi-Agent Preferences PDF

Formal Contracts Mitigate Social Dilemmas in Multi-Agent Reinforcement Learning

A. Haupt‡, P. Christoffersen‡, M. Damani, D. Hadfield-Menell

Autonomous Agents and Multi-Agent Systems 38 (2), p. 1-38, 2024.

Published Multi-Agent Market Design PDF CODE

Black-Box Access is Insufficient for Rigorous AI Audits

S. Casper, C. Ezell, C. Siegmann, N. Kolt, T.L. Curtis, B. Bucknall, A. Haupt, K. Wei, J. Scheurer, M. Hobbhahn, et al.

ACM Conference on Fairness, Accountability, and Transparency, 2024.

Published AI Evaluation PDF

Understanding Multi-Homing and Switching by Platform Drivers

X. Guo‡, A. Haupt‡, H. Wang, R. Qadri, J. Zhao

Transportation Research Part C: Emerging Technologies 154, 2023.

Published Platforms Work PDF

Steering No-Regret Learners to Optimal Equilibria

B.H. Zhang, G. Farina, I. Anagnostides, F. Cacciamani, S.M. McAleer, A. Haupt, A. Celli, N. Gatti, V. Conitzer, T. Sandholm

ACM Conference on Economics and Computation, 2023.

Published Multi-Agent Market Design PDF

Certification Design for a Competitive Market

A. Haupt‡, N. Immorlica‡, B. Lucier‡

ACM Conference on Economics and Computation, 2023.

Published Market Design PDF

Towards Psychologically-Grounded Dynamic Preference Models

M. Curmei‡, A. Haupt‡, B. Recht, D. Hadfield-Menell

ACM Conference on Recommender Systems, 2022.

Published Preferences PDF

The Optimality of Upgrade Pricing

D. Bergemann‡, A. Bonatti‡, A. Haupt‡, A. Smolin‡

Web and Internet Economics, 2021.

Published Market Design PDF

Ongoing Interests

Economic Measurement

SituatedEvals (leading simulacrabench.org), Centaurs (AI-resilient coding assistants, alignment as a centaur evaluation), a new O*NET

Work AI Evaluation

Incentives

Self-Preferencing of LLMs (VZBV evaluation), Multi-Tasking (welfare, improvability, and variance in benchmark aggregation)

Platforms Market Design Multi-Agent

Preference Learning

Simulacra (training models that simulate people), Compressed Individuality (heterogeneity in silicon philosophers), Roles (the role of roles in contextual integrity)

Preferences Privacy Aggregation

Vita

Full Resume and CV are available as pdf.

Andreas Haupt
h4upt@stanford.edu