Balanced News Using Constrained Bandit-based Personalization. - Sayash Kapoor; Vijay Keswani; Nisheeth K. Vishnoi; L. Elisa Celis (IJCAI, 2018)

An Algorithmic Framework to Control Bias in Bandit-based Personalization. - L. Elisa Celis; Sayash Kapoor; Farnood Salehi; Nisheeth K. Vishnoi (CoRR, 2018)

Controlling Polarization in Personalization: An Algorithmic Framework. - L. Elisa Celis; Sayash Kapoor; Farnood Salehi; Nisheeth K. Vishnoi (FAT, 2019)

A dashboard for controlling polarization in personalization. - L. Elisa Celis; Sayash Kapoor; Farnood Salehi; Vijay Keswani; Nisheeth K. Vishnoi (AI Commun., 2019)

Corruption-tolerant bandit learning. - Sayash Kapoor; Kumar Kshitij Patel; Purushottam Kar (Mach. Learn., 2019)

The platform as the city. - Mac Andre Arboleda; Palak Dudani; Sayash Kapoor; Lorna Xu (Interactions, 2021)

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 (AIES, 2022)

Leakage and the Reproducibility Crisis in ML-based Science. - Sayash Kapoor; Arvind Narayanan (CoRR, 2022)

Weaving Privacy and Power: On the Privacy Practices of Labor Organizers in the U.S. Technology Industry. - Sayash Kapoor; Matthew Sun; Mona Wang; Klaudia Jazwinska; Elizabeth Anne Watkins (Proc. ACM Hum. Comput. Interact., 2022)

Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy. - Angelina Wang; Sayash Kapoor; Solon Barocas; Arvind Narayanan (FAccT, 2023)

REFORMS: Reporting Standards for Machine Learning Based Science. - Sayash Kapoor; Emily Cantrell; Kenny Peng; Thanh Hien Pham; Christopher A. Bail; Odd Erik Gundersen; Jake M. Hofman; Jessica Hullman; Michael A. Lones; Momin M. Malik; Priyanka Nanayakkara; Russell A. Poldrack; et al. (CoRR, 2023)

The Foundation Model Transparency Index. - Rishi Bommasani; Kevin Klyman; Shayne Longpre; Sayash Kapoor; Nestor Maslej; Betty Xiong; Daniel Zhang; Percy Liang (CoRR, 2023)

Leakage and the reproducibility crisis in machine-learning-based science. - Sayash Kapoor; Arvind Narayanan (Patterns, 2023)

Foundation Model Transparency Reports. - Rishi Bommasani; Kevin Klyman; Shayne Longpre; Betty Xiong; Sayash Kapoor; Nestor Maslej; Arvind Narayanan; Percy Liang (AIES (1), 2024)

Position: On the Societal Impact of Open Foundation Models. - Sayash Kapoor; Rishi Bommasani; Kevin Klyman; Shayne Longpre; Ashwin Ramaswami; Peter Cihon; Aspen K. Hopkins; Kevin Bankston; Stella Biderman; Miranda Bogen; Rumman Chowdhury; Alex Engler; et al. (ICML, 2024)

Position: A Safe Harbor for AI Evaluation and Red Teaming. - Shayne Longpre; Sayash Kapoor; Kevin Klyman; Ashwin Ramaswami; Rishi Bommasani; Borhane Blili-Hamelin; Yangsibo Huang; Aviya Skowron; Zheng Xin Yong; Suhas Kotha; Yi Zeng 005; Weiyan Shi; et al. (ICML, 2024)

Promises and pitfalls of artificial intelligence for legal applications. - Sayash Kapoor; Peter Henderson 002; Arvind Narayanan (CoRR, 2024)

A Safe Harbor for AI Evaluation and Red Teaming. - Shayne Longpre; Sayash Kapoor; Kevin Klyman; Ashwin Ramaswami; Rishi Bommasani; Borhane Blili-Hamelin; Yangsibo Huang; Aviya Skowron; Zheng Xin Yong; Suhas Kotha; Yi Zeng 005; Weiyan Shi; et al. (CoRR, 2024)

On the Societal Impact of Open Foundation Models. - Sayash Kapoor; Rishi Bommasani; Kevin Klyman; Shayne Longpre; Ashwin Ramaswami; Peter Cihon; Aspen K. Hopkins; Kevin Bankston; Stella Biderman; Miranda Bogen; Rumman Chowdhury; Alex Engler; et al. (CoRR, 2024)

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; Mérouane Debbah; Sayash Kapoor; Yann LeCun; Mark Surman; Helen King-Turvey; Nathan Lambert 001; et al. (CoRR, 2024)

The Foundation Model Transparency Index v1.1: May 2024. - Rishi Bommasani; Kevin Klyman; Sayash Kapoor; Shayne Longpre; Betty Xiong; Nestor Maslej; Percy Liang (CoRR, 2024)

CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark. - Zachary S. Siegel; Sayash Kapoor; Nitya Nagdir; Benedikt Stroebl; Arvind Narayanan (CoRR, 2024)

Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect Verifiers. - Benedikt Stroebl; Sayash Kapoor; Arvind Narayanan (CoRR, 2024)

The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources. - Shayne Longpre; Stella Biderman; Alon Albalak; Hailey Schoelkopf; Daniel McDuff; Sayash Kapoor; Kevin Klyman; Kyle Lo; Gabriel Ilharco; Nay San; Maribeth Rauh; Aviya Skowron; et al. (Trans. Mach. Learn. Res., 2024)

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 (Trans. Mach. Learn. Res., 2024)

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; et al. (FAccT, 2025)

The 2024 Foundation Model Transparency Index. - Rishi Bommasani; Kevin Klyman; Sayash Kapoor; Shayne Longpre; Betty Xiong; Nestor Maslej; Percy Liang (Trans. Mach. Learn. Res., 2025)

The 2023 Foundation Model Transparency Index. - Rishi Bommasani; Kevin Klyman; Shayne Longpre; Sayash Kapoor; Nestor Maslej; Betty Xiong; Daniel Zhang; Percy Liang (Trans. Mach. Learn. Res., 2025)

AI Agents That Matter. - Sayash Kapoor; Benedikt Stroebl; Zachary S. Siegel; Nitya Nadgir; Arvind Narayanan (Trans. Mach. Learn. Res., 2025)

International AI Safety Report. - Yoshua Bengio; Sören Mindermann; Daniel Privitera; Tamay Besiroglu; Rishi Bommasani; Stephen Casper; Yejin Choi 001; Philip Fox; Ben Garfinkel; Danielle Goldfarb; Hoda Heidari; Anson Ho; et al. (CoRR, 2025)

In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI. - Shayne Longpre; Kevin Klyman; Ruth E. Appel; Sayash Kapoor; Rishi Bommasani; Michelle Sahar; Sean McGregor; Avijit Ghosh; Borhane Blili-Hamelin; Nathan Butters; Alondra Nelson; Amit Elazari; et al. (CoRR, 2025)

The Leaderboard Illusion. - Shivalika Singh; Yiyang Nan; Alex Wang; Daniel D'souza; Sayash Kapoor; Ahmet Üstün; Sanmi Koyejo; Yuntian Deng; Shayne Longpre; Noah Smith; Beyza Ermis; Marzieh Fadaee; et al. (CoRR, 2025)

Resist Platform-Controlled AI Agents and Champion User-Centric Agent Advocates. - Sayash Kapoor; Noam Kolt; Seth Lazar (CoRR, 2025)

A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety. - Camille François; Ludovic Peran; Ayah Bdeir; Nouha Dziri; Will Hawkins; Yacine Jernite; Sayash Kapoor; Juliet Shen; Heidy Khlaaf; Kevin Klyman; Nik Marda; Marie Pellat; et al. (CoRR, 2025)

Establishing Best Practices for Building Rigorous Agentic Benchmarks. - Yuxuan Zhu; Tengjun Jin; Yada Pruksachatkun; Andy Zhang; Shu Liu; Sasha Cui; Sayash Kapoor; Shayne Longpre; Kevin Meng; Rebecca Weiss; Fazl Barez; Rahul Gupta 001; et al. (CoRR, 2025)

Bridging Prediction and Intervention Problems in Social Systems. - Lydia T. Liu; Inioluwa Deborah Raji; Angela Zhou; Luke Guerdan; Jessica Hullman; Daniel Malinsky; Bryan Wilder; Simone Zhang; Hammaad Adam; Amanda Coston; Benjamin Laufer; Ezinne Nwankwo; et al. (CoRR, 2025)