☑ represents peer-reviewed papers
| Book | ☑ AI
Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the
Difference
Arvind Narayanan, Sayash Kapoor Princeton University Press (2024) Featured in Nature's list of the 10 best books of 2024 |
| Preprint | International AI Safety Report
Yoshua Bengio, ..., Sayash Kapoor et al. Preprint (2025) A report on the state of advanced AI capabilities and risks written by 100 AI experts |
| Preprint | AI
Agents That Matter · Blog post
Sayash Kapoor*, Benedikt Stroebl*, Zachary S. Siegel, Nitya Nadgir, Arvind Narayanan Preprint (2024) |
| Preprint | Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect Verifiers
Benedikt Stroebl, Sayash Kapoor, Arvind Narayanan Preprint (2024) |
| Preprint | The Reality of AI and Biorisk
Aidan Peppin, Anka Reuel, Stephen Casper, Elliot Jones, Andrew Strait, Usman Anwar, Anurag Agrawal, Sayash Kapoor, Sanmi Koyejo, Marie Pellat, Rishi Bommasani, Nick Frosst, Sara Hooker Preprint (2024) |
| Journal | ☑ Considerations for governing open foundation models
Rishi Bommasani, Sayash Kapoor, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Daniel Zhang, Marietje Schaake, Daniel E. Ho, Arvind Narayanan, Percy Liang Science (2024) |
| Journal | ☑ REFORMS: Consensus-based Recommendations 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 (2024) |
| Conference | ☑ 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 International Conference on Machine Learning (ICML 2024 Oral) |
| Conference | ☑ 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 International Conference on Machine Learning (ICML 2024 Oral) Our open letter to AI companies calling for a safe harbor was signed by over 350 academics, researchers, and civil society members. |
| Journal | ☑ How large language models can reshape collective intelligence Jason W. Burton, Ezequiel Lopez-Lopez, Shahar Hechtlinger, Zoe Rahwan, Samuel Aeschbach, Michiel A. Bakker, Joshua A. Becker, Aleks Berditchevskaia, Julian Berger, Levin Brinkmann, Lucie Flek, Stefan M. Herzog, Saffron Huang, Sayash Kapoor, Arvind Narayanan et al. Nature Human Behaviour (2024) |
| Journal | ☑ The 2024 Foundation Model Transparency Index Rishi Bommasani, Kevin Klyman, Sayash Kapoor, Shayne Longpre, Betty Xiong, Nestor Maslej, Percy Liang Transactions on Machine Learning Research (TMLR 2025) |
| Journal | ☑ CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark Zachary S. Siegel, Sayash Kapoor, Nitya Nadgir, Benedikt Stroebl, Arvind Narayanan Transactions on Machine Learning Research (TMLR 2025) |
| Journal | ☑ The 2023 Foundation Model Transparency Index Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Daniel Zhang, Percy Liang Transactions on Machine Learning Research (TMLR 2025 Featured certification) |
| Journal | ☑ The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources 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 Transactions on Machine Learning Research (TMLR 2024 Survey certification) |
| Preprint | Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence Adrien Basdevant, Camille François, Victor Storchan, Kevin Bankston, Ayah Bdeir, Brian Behlendorf, Merouane Debbah, Sayash Kapoor, Yann LeCun, Mark Surman, Helen King-Turvey, Nathan Lambert, Stefano Maffulli, Nik Marda, Govind Shivkumar, Justine Tunney Preprint (2024) |
| Journal | ☑ Promises and pitfalls of artificial intelligence for legal applications · Blog post
Sayash Kapoor, Peter Henderson, Arvind Narayanan Journal of Cross-disciplinary Research in Computational Law (CRCL 2024) |
| 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 (JCR 2024) Also presented at: Philosophy, AI, and Society (2023); Data (Re)Makes the World (2023), ACM FAccT (2023) |
| Conference | ☑ Foundation Model Transparency
Reports · Blog post
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES 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) |
| 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 (PNAS 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 (CSCW 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 (AIES 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) |