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Research Papers

IDE researchers publish their findings and work in highly respected academic journals.

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Research Papers Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Peter Slattery | Research Scientist at MIT Future Tech

Hans Gundlach | Research Assistant, CSAIL

Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL

Alexander K. Saeri

Jess Graham

Michael Noetel, et al.

 

This paper reports results from a three-round Delphi study conducted late 2025 with 272 international AI experts who rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.

Research Papers Advancing AI negotiations: A large-scale autonomous negotiation competition

Jun 2026

Sinan Aral | Director, MIT IDE; Professor, MIT Sloan School of Management

Harang Ju | Assistant Professor, Johns Hopkins University

Michelle Vaccaro

Michael Caosun

Jared R. Curhan

 

This research reveals that principles from human negotiation theory remain crucial even in AI–AI contexts and that AI-specific technical strategies also create success. The results suggest the need to establish a new theory of AI negotiation that integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.

Research Papers The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

Peter Slattery | Research Scientist at MIT Future Tech

Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL

 

Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet researchers, policymakers, and technology companies lack shared terminology for discussing AI risks. This paper addresses this challenge by creating a comprehensive catalog of AI risks. 

Research Papers Teaching AI to handle exceptions: Supervised fine-tuning with human-aligned judgment

May 2026

Sinan Aral | Director, MIT IDE; Professor, MIT Sloan School of Management

Harang Ju | Assistant Professor, Johns Hopkins University

These findings highlight the need to address LLMs’ shortcomings in handling exceptions in order to guide the development of agentic AI toward models that can effectively align with human judgment and simultaneously adapt to novel contexts.

Research Papers Prompt Adaptation as a Dynamic Complement in Generative AI Systems

Apr 2026

David Holtz | Assistant Professor, Columbia Business School

Benjamin Manning | Ph.D. Candidate

Eaman Jahani | Assistant Professor, Robert H. Smith School of Business, University of Maryland

Hong-Yi Tu Ye | PhD Candidate

Joe Zhang | Stanford University
Mohammed Alsobay | Microsoft Research
Christos Nicolaides | University of Cyprus
Siddharth Suri | Microsoft Research
This paper studies prompt adaptation—how users adjust their inputs in response to evolving model behavior—using a common experimental design applied to two preregistered tasks with 3,750 total participants who submitted nearly 37,000 prompts.