Neel Guha

Associate Professor of Law

Full-time Faculty

Education

Ph.D., Stanford University, 2026
J.D., Stanford Law School, 2026
M.S., Carnegie Mellon University, 2019
B.S., Stanford University, 2018

Areas of Study

Artificial Intelligence and Technology Law

Areas of Specialty

Torts
Law and Artificial Intelligence
Empirical Analysis of Law
Regulation

Neel Guha is a computer scientist and legal scholar who studies the implications of AI for the law. His research focuses on AI’s increasingly transformative impacts on the legal profession; the novel challenges AI poses for courts and regulators; and the use of AI for large-scale empirical analysis of the law. He joined Columbia Law School as an associate professor of law on July 1, 2026.

Guha’s work has been published in law reviews, academic journals, and peer-reviewed machine learning conferences, including Harvard Journal of Law & Technology, The New England Journal of Medicine, University of Pennsylvania Law Review, Proceedings of the National Academy of Sciences, NeurIPS, ICML, and ICLR. He has presented his work around the world and has received numerous awards for his papers, including the Carole Hafner Best Paper Award at the 2021 International Conference on Artificial Intelligence and Law. His research on AI evaluation has been widely adopted within the legal technology industry, and his early writing for frontier model adverse event reporting has been influential in contemporary AI policy.

Guha is in the final stages of completing a Ph.D. in computer science from Stanford University. During law school, he served as the senior articles editor for the Stanford Law Review.

Visit his personal website here.

Publications

Legal Publications

There’s No Free Benchmark: An Institutional View of Legal AI Benchmarking.; Neel Guha, Andy K. Zhang, Christine Tsang, Christopher D. Manning, Julian Nyarko, and Daniel E. Ho; Proceedings of the National Academy of Sciences
The State Statutes Project, 2024 Wis. L. Rev. 1615, Neel Guha and Diego Zambrano
AI Regulation Has Its Own Alignment Problem: The Technical and Institutional Feasibility of Disclosure, Registration, Licensing, and Auditing; 92 George Washington Law Review 1473 (2024) ; Neel Guha*, Christie M. Lawrence*, Lindsey A. Gailmard, Kit T. Rodolfa, Faiz Surani, Rishi Bommasani, Inioluwa Deborah Raji, Mariano-Florentino Cuéllar, Colleen Honigsberg, Percy Liang, and Daniel E. Ho
Understanding Liability Risk from Using Health Care Artificial Intelligence Tools, New England Journal of Medicine (2024), Michelle M. Mello and Neel Guha
Private Enforcement in the States; 172 University of Pennsylvania Law Review 61 (2023); Diego Zambrano, Neel Guha, Austin Peters, and Jeffrey Xia
ChatGPT and Physicians’ Malpractice Risk, JAMA Health Forum (2023), Michelle M. Mello and Neel Guha
Vulnerabilities in Discovery Tech; 35 Harvard Journal of Law & Technology 281 (2022); Neel Guha, Peter Henderson, and Diego Zambrano

Computer Science Publications

Cartridges: Lightweight and General-purpose Long Context Representations via Self-Study; International Conference on Learning Representations (2026); Sabri Eyuboglu*, Ryan Ehrlich*, Simran Arora*, Neel Guha, Dylan Zinsley, Emily Liu, Will Tennien, Atri Rudra, James Zou, Azalia Mirhoseini, Christopher Ré
An Architecture Search Framework for Inference-Time Techniques; International Conference on Machine Learning (2025); Jon Saad-Falcon, Adrian Gamarra Lafuente, Shlok Natarajan, Nahum Maru, Hristo Todorov, Etash Guha, E. Kelly Buchanan, Mayee Chen, Neel Guha, Christopher Ré, Azalia Mirhoseini
Open Problems in Technical AI Governance; Transactions on Machine Learning Research (2025); Anka Reuel*, Ben Bucknall*, Stephen Casper, Tim Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Alexandra Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, Robert Trager
A Reasoning-Focused Legal Retrieval Benchmark; 4th ACM Symposium on Computer Science and Law (2025); Lucia Zheng*, Neel Guha*, Javokhir Arifov, Sarah R Zhang, Michal Skreta, Christopher D Manning, Peter Henderson, Daniel E. Ho
Smoothie: Label Free Language Model Routing; Conference on Neural Information Processing Systems (2024); Neel Guha*, Mayee F. Chen*, Trevor Chow, Ishan S. Khare, Christopher Ré
Stronger Than You Think: Benchmarking Weak Supervision on Realistic Tasks; Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (2024); Also appeared in: Workshop on Data-Centric Machine Learning Research at ICML (2024); Tianyi Zhang, Linrong Cai, Jeffrey Li, Nicholas Roberts, Neel Guha, Frederic Sala
Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT; International Conference on Machine Learning (2024); Also appeared in: Workshop on Mathematical and Empirical Understanding of Foundation Models at ICLR (2024); Jon Saad-Falcon, Daniel Y. Fu, Simran Arora, Neel Guha, Christopher Ré
Prospector Heads: Generalized Feature Attribution for Large Models & Data; International Conference on Machine Learning (2024); Gautam Machiraju*, Alexander Derry*, Arjun Desai, Neel Guha, Amir-Hossein Karimi, James Zou, Russ Altman, Christopher Ré, Parag Mallick
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models; Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (2023); Neel Guha*, Julian Nyarko*, Daniel E. Ho*, Christopher Ré*, Adam Chilton, Aditya Narayana, Alex ChohlasWood, Austin Peters, Brandon Waldon, Daniel N. Rockmore, and Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael Livermore, Nikon Rasumov Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, and Zehua Li
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification; Conference on Neural Information Processing Systems (2023); Neel Guha*, Mayee F. Chen*, Kush Bhatia*, Azalia Mirhoseini, Frederic Sala, and Christopher Ré
Don’t Use a Cannon to Kill a Fly: An Efficient Cascading Pipeline for Long Documents; International Conference on AI and Law (2023); Zehua Li, Neel Guha, and Julian Nyarko
Holistic Evaluation of Language Models; Transactions on Machine Learning Research (2023); Percy Liang*, Rishi Bommasani*, Tony Lee*, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, and Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Alexander Cosgrove, Christopher D Manning, Christopher Re, Diana AcostaNavas, Drew Arad Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue WANG, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Andrew Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda
Ask Me Anything: A Simple Strategy for Prompting Language Models; International Conference on Learning Representations (2023) (Spotlight); Simran Arora*, Avanika Narayan*, Mayee F Chen, Laurel J Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open Source Legal Dataset; Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (2022) (Oral Presentation); Peter Henderson*, Mark S. Krass*, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, Daniel E. Ho
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the Case HOLD Dataset; International Conference on AI and Law (2021) (Carole Hafner Best Paper Award); Lucia Zheng*, Neel Guha*, Brandon R. Anderson, Peter Henderson, Daniel E. Ho
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation; Conference on Innovative Data Systems Research (2021); Laurel Orr*, Megan Leszczynski*, Simran Arora, Sen Wu, Neel Guha, Xiao Ling, Christopher Ré
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy; ACM Conference on Fairness, Accountability, and Transparency (2020); Amanda Coston, Neel Guha, Lisa Lu, Derek Ouyang, Alexandra Chouldechova, and Daniel E. Ho
Machine Learning for AC Optimal Power Flow; Climate Change Workshop at the International Conference on Machine Learning (2019) (Honorable Mention for Best Paper); Neel Guha, Zhecheng Wang, Matt Wytock, and Arun Majumdar
One-Shot Federated Learning; 2nd Workshop on Machine Learning on the Phone and other Consumer Devices at Neural Information Processing Systems (2018) (Oral Presentation); Neel Guha, Ameet Talwalkar, and Virginia Smith

White Papers

HAI Policy Brief: Understanding Liability Risk from Healthcare AI (2024); Michelle M. Mello and Neel Guha
HAI Policy Brief: The AI Regulatory Alignment Problem (2023); Neel Guha*, Christie M. Lawrence*, Lindsey A. Gailmard, Kit T. Rodolfa, Faiz Surani, Rishi Bommasani, Inioluwa Deborah Raji, Mariano-Florentino Cuéllar, Colleen Honigsberg, Percy Liang, and Daniel E. Ho
Building a National AI Research Resource: A Blueprint for the National Research Cloud (2021), Contributing Author: §5 (Data Privacy Compliance), §6 (Technical Privacy and Virtual Data Safe Rooms), §8 (Managing Cybersecurity Risks).
On the Opportunities and Risks of Foundation Models (2021), Contributing Author: §3.2 (Legal Applications) and §5.4 (Legality)
ResX Task Force Final Report: Our Vision for Stanford’s Undergraduate Residences (2018), ResX Task Force

Book Chapters

AI Assurance; In Equalizing Justice: Harnessing AI for Litigants Without Lawyers (Cambridge Univ. Press, forthcoming July 2026), Neel Guha and Izak C. Rosenfeld
Building GenAI Benchmarks: A Case Study in Legal Applications; In The Oxford Handbook on the Foundations and Regulation of Generative AI (Oxford University Press, 2025); Neel Guha, Julian Nyarko, Daniel E. Ho, and Christopher Ré
Gamesmanship in Modern Discovery Tech; In Legal Tech and the Future of Civil Justice (Cambridge University Press, 2023); Diego Zambrano, Neel Guha, and Peter Henderson

Honors and Awards

The National Law Review / Wickard Top 50 Legal Innovators in Academia

2026

Stanford Interdisciplinary Graduate Fellowship

2023

Stanford HAI Graduate Fellowship

2023

Carole Hafner Best Paper Award, International Conference on Artificial Intelligence and Law

2021

Gerald Gunther Prize for Outstanding Performance in Health Law

2021

John Hart Ely Prize for Outstanding Performance in a Policy Lab (“Creating a National Research Cloud”)

2021

Best Paper Honorable Mention, ICML Climate Change Workshop at the International Conference on Machine Learning

2019

Select News and Media

Gizmodo: “Google’s AI Will Help Decide Whether Unemployed Workers Get Benefits”
Emergency Department Management: “Malpractice Claims Involving AI Tools Are Emerging: EDs Can Mitigate Legal Exposure”
Stanford HAI: “Who’s at Fault When AI Fails in Health Care?”
Politico: “Who Pays When AI Goes Rogue?”
Stanford Medicine Scope: “Research Explores Liability Risk of Using AI Tools in Patient Care”
The Stanford Lawyer: “Artificial Intelligence and the Law”
New Scientist: “GPT-4 Wins Chatbot Lawyer Contest but Is Still Not as Good as Humans”
The Volokh Conspiracy: “Private Enforcement in the States, by Prof. Diego Zambrano (Stanford) et al.”
Law360: “A ‘Messy’ Tangle of Private Right Enforcement In State Law”
Bloomberg Law: “Federal Use of AI Tools Prompts Researchers to Build New Dataset”
Stanford HAI: “Borrowing From the Law to Filter Training Data for Foundation Models”
The Stanford Lawyer: “And Justice for All: Improving Access Through Digital Tools, Innovative Design”
The Wall Street Journal: “Smartphone Location Data Can Leave Out Those Most Hit by Covid-19”

Activities and Affiliations

Healthcare AI Policy Steering Committee, Stanford Human-Centered AI Institute, 2025
Senior Articles Editor, Stanford Law Review (Volume 75), 2022
Member Editor, Stanford Law Review (Volume 74), 2021
ResX, Provostial Task Force, Stanford University, 2018
Committee on Residential Learning, Stanford University, 2017–2018