Summary of A Safe Harbor For Ai Evaluation and Red Teaming, by Shayne Longpre et al.

A Safe Harbor for AI Evaluation and Red Teaming

by 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

First submitted to arxiv on: 7 Mar 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper highlights the importance of independent evaluation and red teaming for identifying risks posed by generative AI systems. However, current terms of service and enforcement strategies used by prominent AI companies have disincentives on good faith safety evaluations, leading some researchers to fear account suspensions or legal reprisal. To address this issue, we propose that major AI developers commit to providing a safe harbor for public interest safety research, indemnifying it from account suspensions or legal reprisal. This proposal emerges 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. Our goal is to create a more inclusive and unimpeded community effort to tackle the risks of generative AI.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper talks about the importance of checking if artificial intelligence (AI) systems are safe and not harmful. However, some big companies that make AI don’t let researchers do this kind of work without worrying about getting in trouble. To fix this problem, we suggest that these companies promise to protect researchers who want to check their AI for safety issues. This would help the community come together to find ways to keep AI safe and trustworthy.