Publications

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☑ represents peer-reviewed papers

Journal REFORMS: Reporting Standards for Machine Learning Based Science · Blog post
Sayash Kapoor, Emily Cantrell, Kenny Peng, Thanh Hien (Hien) Pham, Christopher A. Bail, Odd Erik Gundersen, Jake M. Hofman, Jessica Hullman, Michael A. Lones, Momin M. Malik, Priyanka Nanayakkara, Russell A. Poldrack, Inioluwa Deborah Raji, Michael Roberts, Matthew J. Salganik, Marta Serra-Garcia, Brandon M. Stewart, Gilles Vandewiele, Arvind Narayanan
Science Advances (forthcoming)
Preprint On the Societal Impact of Open Foundation Models · Blog post
Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Peter Cihon, Aspen Hopkins, Kevin Bankston, Stella Biderman, Miranda Bogen, Rumman Chowdhury, Alex Engler, Peter Henderson, Yacine Jernite, Seth Lazar, Stefano Maffulli, Alondra Nelson, Joelle Pineau, Aviya Skowron, Dawn Song, Victor Storchan, Daniel Zhang, Daniel E. Ho, Percy Liang, Arvind Narayanan
Preprint (2024)
Journal Promises and pitfalls of large language models for legal professionals and lay people · Blog post
Sayash Kapoor, Peter Henderson, Arvind Narayanan
Invited publication, to appear in the Journal of Cross-disciplinary Research in Computational Law (forthcoming)
Journal Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy · Blog post
Angelina Wang*, Sayash Kapoor*, Solon Barocas, Arvind Narayanan
ACM Journal on Responsible Computing (2024)
Also presented at: Philosophy, AI, and Society (2023); Data (Re)Makes the World (2023), ACM Conference on Fairness, Accountability, and Transparency (2023)
Preprint Foundation Model Transparency Reports · Blog post
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang
Preprint (2024)
Preprint A Safe Harbor for AI Evaluation and Red Teaming · Blog post
Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng-Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson
Preprint (2024)
Our open letter to AI companies calling for a safe harbor was signed by over 350 academics, researchers, and civil society members.
Preprint The Foundation Model Development Cheatsheet
Shayne Longpre, Stella Biderman, Alon Albalak, Gabriel Ilharco, Sayash Kapoor, Kevin Klyman, Kyle Lo, Maribeth Rauh, Nay San, Hailey Schoelkopf, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini
Preprint (2024)
Journal Leakage and the reproducibility crisis in ML-based science
Sayash Kapoor, Arvind Narayanan
Patterns (2023)
Policy brief Considerations for Governing Open Foundation Models · Blog post
Rishi Bommasani, Sayash Kapoor, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Daniel Zhang, Marietje Schaake, Daniel E. Ho, Arvind Narayanan, Percy Liang
Stanford HAI Issue Brief (2023)
Preprint The Foundation Model Transparency Index
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang
Preprint (2023)
Journal The limitations of machine learning models for predicting scientific replicability
M. J. Crockett, Xuechunzi Bai, Sayash Kapoor, Lisa Messeri, and Arvind Narayanan
Proceedings of the National Academy of Sciences (2023)
Online essay How to Prepare for the Deluge of Generative AI on Social Media
Sayash Kapoor, Arvind Narayanan
Knight First Amendment Institute (2023)
Conference Weaving Privacy and Power: On the Privacy Practices of Labor Organizers in the U.S. Technology Industry
Sayash Kapoor*, Matthew Sun*, Mona Wang*, Klaudia Jaźwińska*, Elizabeth Anne Watkins*
ACM Conference on Computer-Supported Cooperative Work and Social Computing (2022)
🏆 Impact Recognition Award
Conference The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan
ACM Conference on AI, Ethics, and Society (2022)
Conference Controlling polarization in personalization: an algorithmic framework
L. Elisa Celis, Sayash Kapoor, Farnood Salehi, and Nisheeth K. Vishnoi
ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2019
🏆 Best Paper Award
Journal Corruption-tolerant bandit learning
Sayash Kapoor, Kumar Kshitij Patel, and Purushottam Kar
Machine Learning (2019)
Journal A dashboard for controlling polarization in personalization
L. Elisa Celis, Sayash Kapoor, Vijay Keswani, Farnood Salehi, and Nisheeth K. Vishnoi
AI Communications (2019)
Conference Balanced news using constrained bandit-based personalization
Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi, and L. Elisa Celis
IJCAI Demos Track (2018)