Sayash Kapoor

According to our database, Sayash Kapoor authored at least 17 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

On the Societal Impact of Open Foundation Models.

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

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, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson

CoRR, 2024

Foundation Model Transparency Reports.

Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang

CoRR, 2024

Promises and pitfalls of artificial intelligence for legal applications.

Sayash Kapoor, Peter Henderson, Arvind Narayanan

CoRR, 2024

2023

Leakage and the reproducibility crisis in machine-learning-based science.

Sayash Kapoor, Arvind Narayanan

Patterns, September, 2023

The Foundation Model Transparency Index.

Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang

CoRR, 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, Inioluwa Deborah Raji, Michael Roberts, Matthew J. Salganik, Marta Serra-Garcia, Brandon M. Stewart, Gilles Vandewiele, Arvind Narayanan

CoRR, 2023

Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy.

Angelina Wang, Sayash Kapoor, Solon Barocas, Arvind Narayanan

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

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

Leakage and the Reproducibility Crisis in ML-based Science.

Sayash Kapoor, Arvind Narayanan

CoRR, 2022

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

Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

2021

The platform as the city.

Mac Andre Arboleda, Palak Dudani, Sayash Kapoor, Lorna Xu

Interactions, 2021

2019

Corruption-tolerant bandit learning.

Sayash Kapoor, Kumar Kshitij Patel, Purushottam Kar

Mach. Learn., 2019

A dashboard for controlling polarization in personalization.

L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Vijay Keswani, Nisheeth K. Vishnoi

AI Commun., 2019

Controlling Polarization in Personalization: An Algorithmic Framework.

L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Nisheeth K. Vishnoi

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

An Algorithmic Framework to Control Bias in Bandit-based Personalization.

L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Nisheeth K. Vishnoi

CoRR, 2018

Balanced News Using Constrained Bandit-based Personalization.

Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi, L. Elisa Celis

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018