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A Safe Harbor for AI Evaluation and Red Teaming

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arxiv 2403.04893 v1 pith:WE5D6N34 submitted 2024-03-07 cs.AI

classification cs.AI
keywords researchgenerativelegalsafetyaccessaccountcommunitycompanies
verification ladder T0 review T1 audit T2 compute T3 formal
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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 AI developers commit to providing a legal and technical safe harbor, indemnifying public interest safety research and protecting it from 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.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Legal and ethical bans on CSAM access and generation break standard AI safety techniques, creating 15 open problems that demand new methods for dataset cleaning, concept fusion prevention, fine-tuning resilience, dete...

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  3. Adversarial Attacks on Robotic Vision Language Action Models

    cs.RO 2025-06 conditional novelty 6.0 of 10

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  4. GuardVal: Dynamic Large Language Model Jailbreak Evaluation for Comprehensive Safety Testing

    cs.LG 2025-07 reject novelty 4.0 of 10

    GuardVal combines role-playing jailbreak generation with an Adam-inspired optimizer and an Overall Safety Value metric, but the method is underspecified and not validated with released code or data.

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