TY - JOUR
T1 - Position
T2 - 41st International Conference on Machine Learning, ICML 2024
AU - Longpre, Shayne
AU - Kapoor, Sayash
AU - Klyman, Kevin
AU - Ramaswami, Ashwin
AU - Bommasani, Rishi
AU - Blili-Hamelin, Borhane
AU - Huang, Yangsibo
AU - Skowron, Aviya
AU - Yong, Zheng Xin
AU - Kotha, Suhas
AU - Zeng, Yi
AU - Shi, Weiyan
AU - Yang, Xianjun
AU - Robey, Reid Southen Alexander
AU - Chao, Patrick
AU - Yang, Diyi
AU - Jia, Ruoxi
AU - Kang, Daniel
AU - Pentland, Sandy
AU - Narayanan, Arvind
AU - Liang, Percy
AU - Henderson, Peter
N1 - Publisher Copyright:
Copyright 2024 by the author(s)
PY - 2024
Y1 - 2024
N2 - Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researchers to fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. Although some companies offer researcher access programs, they are an inadequate substitute for independent research access, as they have limited community representation, receive inadequate funding, and lack independence from corporate incentives. We propose that major generative AI developers commit to providing a legal and technical safe harbor, protecting public interest safety research and removing the threat of account suspensions or legal reprisal. These proposals emerged from our collective experience conducting safety, privacy, and trustworthiness research on generative AI systems, where norms and incentives could be better aligned with public interests, without exacerbating model misuse. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
AB - Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researchers to fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. Although some companies offer researcher access programs, they are an inadequate substitute for independent research access, as they have limited community representation, receive inadequate funding, and lack independence from corporate incentives. We propose that major generative AI developers commit to providing a legal and technical safe harbor, protecting public interest safety research and removing the threat of account suspensions or legal reprisal. These proposals emerged from our collective experience conducting safety, privacy, and trustworthiness research on generative AI systems, where norms and incentives could be better aligned with public interests, without exacerbating model misuse. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
UR - https://www.scopus.com/pages/publications/85203814652
UR - https://www.scopus.com/pages/publications/85203814652#tab=citedBy
M3 - Conference article
AN - SCOPUS:85203814652
SN - 2640-3498
VL - 235
SP - 32691
EP - 32710
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
Y2 - 21 July 2024 through 27 July 2024
ER -