用隐私技术让外部专家安全审查AI系统，既保隐私又促透明。
Enabling External Scrutiny of AI Systems with Privacy-Enhancing Technologies
- 整合多种隐私增强技术，构建可审计的AI审查框架。
- 已在真实场景中支持英国和新西兰的AI治理项目。
- 适合政策制定者、研究机构和关注AI透明度的团队。
本文介绍非营利组织OpenMined开发的技术基础设施如何在不泄露敏感信息的前提下，实现对AI系统的外部审查。外部独立审查是保障AI发展透明性的关键环节，但现实中因企业对安全、隐私和知识产权的顾虑，研究人员难以获取访问权限。如今，隐私增强技术（PETs）已达到新成熟度：OpenMined构建的端到端基础设施将多种PETs结合，形成可支持隐私保护审计的多种部署方案。文中展示两个实际应用案例：与‘基督城倡议’合作分析社交媒体推荐算法，以及与英国人工智能安全研究所共同评估前沿模型。文章还描述了当前及未来可实现的审查类型。结论认为，此类创新方法值得进一步探索与支持。建议政策制定者从法律层面赋能研究人员。
原文摘要 · Abstract (English)
This article describes how technical infrastructure developed by the nonprofit OpenMined enables external scrutiny of AI systems without compromising sensitive information. Independent external scrutiny of AI systems provides crucial transparency into AI development, so it should be an integral component of any approach to AI governance. In practice, external researchers have struggled to gain access to AI systems because of AI companies' legitimate concerns about security, privacy, and intellectual property. But now, privacy-enhancing technologies (PETs) have reached a new level of maturity: end-to-end technical infrastructure developed by OpenMined combines several PETs into various setups that enable privacy-preserving audits of AI systems. We showcase two case studies where this infrastructure has been deployed in real-world governance scenarios: "Understanding Social Media Recommendation Algorithms with the Christchurch Call" and "Evaluating Frontier Models with the UK AI Safety Institute." We describe types of scrutiny of AI systems that could be facilitated by current setups and OpenMined's proposed future setups. We conclude that these innovative approaches deserve further exploration and support from the AI governance community. Interested policymakers can focus on empowering researchers on a legal level.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。