Sayash Kapoor

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 25

Top Topics

Predictive Accuracy

News Recommendation

Interdisciplinary Field

Top Venues

Proc. ACM Hum. Comput. Interact.

Publications

Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang
Foundation Model Transparency Reports. CoRR (2024)
Sayash Kapoor, Peter Henderson, Arvind Narayanan
Promises and pitfalls of artificial intelligence for legal applications. CoRR (2024)
Rishi Bommasani, Kevin Klyman, Sayash Kapoor, Shayne Longpre, Betty Xiong, Nestor Maslej, Percy Liang
The Foundation Model Transparency Index v1.1: May 2024. CoRR (2024)
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
On the Societal Impact of Open Foundation Models. CoRR (2024)
Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Ifeoluwa Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources. CoRR (2024)
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, Stefano Maffulli, Nik Marda, Govind Shivkumar, Justine Tunney
Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence. CoRR (2024)
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
A Safe Harbor for AI Evaluation and Red Teaming. CoRR (2024)
Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, Arvind Narayanan
AI Agents That Matter. CoRR (2024)
Sayash Kapoor, Arvind Narayanan
Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4 (9) (2023)
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
REFORMS: Reporting Standards for Machine Learning Based Science. CoRR (2023)
Angelina Wang, Sayash Kapoor, Solon Barocas, Arvind Narayanan
Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy. FAccT (2023)
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang
The Foundation Model Transparency Index. CoRR (2023)
Sayash Kapoor, Matthew Sun, Mona Wang, Klaudia Jazwinska, Elizabeth Anne Watkins
Weaving Privacy and Power: On the Privacy Practices of Labor Organizers in the U.S. Technology Industry. Proc. ACM Hum. Comput. Interact. 6 (CSCW2) (2022)
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan
The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning. AIES (2022)
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning. CoRR (2022)
Sayash Kapoor, Arvind Narayanan
Leakage and the Reproducibility Crisis in ML-based Science. CoRR (2022)
Sayash Kapoor, Matthew Sun, Mona Wang, Klaudia Jazwinska, Elizabeth Anne Watkins
Weaving Privacy and Power: On the Privacy Practices of Labor Organizers in the U.S. Technology Industry. CoRR (2022)
Sayash Kapoor, Matthew Sun, Mona Wang, Klaudia Jazwinska, Elizabeth Anne Watkins
Weaving Privacy and Power: On the Privacy Practices of Labor Organizers in the U.S. Technology Industry. Proc. ACM Hum. Comput. Interact. 6 (CSCW2) (2022)
Mac Andre Arboleda, Palak Dudani, Sayash Kapoor, Lorna Xu
The platform as the city. Interactions 28 (6) (2021)
L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Vijay Keswani, Nisheeth K. Vishnoi
A dashboard for controlling polarization in personalization. AI Commun. 32 (1) (2019)
L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Nisheeth K. Vishnoi
Controlling Polarization in Personalization: An Algorithmic Framework. FAT (2019)
Sayash Kapoor, Kumar Kshitij Patel, Purushottam Kar
Corruption-tolerant bandit learning. Mach. Learn. 108 (4) (2019)
L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Nisheeth K. Vishnoi
An Algorithmic Framework to Control Bias in Bandit-based Personalization. CoRR (2018)
Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi, L. Elisa Celis
Balanced News Using Constrained Bandit-based Personalization. IJCAI (2018)
Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi, L. Elisa Celis
Balanced News Using Constrained Bandit-based Personalization. CoRR (2018)