发表机构
Columbia University; University of Chicago（哥伦比亚大学; 芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究机器遗忘中用户删除请求的行为建模，定义适应性与集体性两种行为，结合随机优化揭示理论差距，并利用数据估值设计请求者以显著影响模型行为，实验验证其差异效果。
AI 中文摘要
机器遗忘被视为一种有前景的方法，使用户能够在人工智能模型背景下行使“被遗忘权”。我们探讨用户行使该权利时如何影响模型行为。我们定义了用户在请求删除其数据时可能采用的两种行为类型：适应性和集体性。通过将用户在此背景下的目标与随机优化结果联系起来，我们展示了表现出这些行为和不表现出这些行为的用户群体之间潜在影响的理论差距。然后，我们展示了如何利用数据估值技术来设计删除请求者，这些请求者能够在现实环境中显著改变模型行为。在计算机视觉任务的实验中，我们展示了不同用户行为模型的差异性影响，并试图分离适应性和集体性的影响。
英文摘要
Machine unlearning is seen as a promising approach to enable users to exercise the "right to erasure" in the context of AI models. We ask how users might influence the behavior of models when exercising this right. We define two types of behaviors that users might adopt when requesting the deletion of their data: adaptivity and collectivity. Drawing connections between the goals of users in this context and results in stochastic optimization, we demonstrate theoretical gaps between the potential effects of groups of users who do and do not display these behaviors. We then show how techniques from data valuation might be used to design deletion requesters that can significantly alter model behavior in realistic settings. In experiments on computer vision tasks, we demonstrate the differential effects of different models of user behavior and attempt to isolate the impacts of adaptivity and collectivity.
Comments17 pages, 7 figures
