论文标题
局外人的监督：设计第三方审计生态系统
Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance
论文作者
论文摘要
很多关注的重点是算法审核和影响评估，以使算法系统的开发人员和用户负责。但是现有的算法问责制政策方法已忽略了非算力领域的课程：值得注意的是，干预措施的重要性允许第三方有效参与。我们的论文综合了其他领域的课程，涉及如何为算法部署制作有效的外部监督系统。首先，我们讨论了当前AI景观中第三方监督的挑战。其次，我们调查跨领域（例如财务，环境和健康法规）的审计系统，并表明此类审核的机构设计远非单一。最后，我们调查围绕这些设计组件的证据基础，并阐明对算法审计的影响。我们得出的结论是，仅审核的转折就不太可能实现实际的算法问责制，并且有意义的第三方参与需要持续关注机构设计。
Much attention has focused on algorithmic audits and impact assessments to hold developers and users of algorithmic systems accountable. But existing algorithmic accountability policy approaches have neglected the lessons from non-algorithmic domains: notably, the importance of interventions that allow for the effective participation of third parties. Our paper synthesizes lessons from other fields on how to craft effective systems of external oversight for algorithmic deployments. First, we discuss the challenges of third party oversight in the current AI landscape. Second, we survey audit systems across domains - e.g., financial, environmental, and health regulation - and show that the institutional design of such audits are far from monolithic. Finally, we survey the evidence base around these design components and spell out the implications for algorithmic auditing. We conclude that the turn toward audits alone is unlikely to achieve actual algorithmic accountability, and sustained focus on institutional design will be required for meaningful third party involvement.
