破解机器学习供应链中数据更正与删除的合规难题
Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains
- 提出'模型在暗处'概念，指下游不可见的衍生模型
- 发现当前技术无法满足GDPR的数据权利要求
- 适合关注AI合规与数据隐私的研究者和从业者
根据《通用数据保护条例》（GDPR），数据主体享有更正与删除的权利，这对保护个人隐私至关重要。然而，在机器学习（ML）系统中有效实施这些权利仍面临挑战。现有研究多从法律或技术单一视角出发，忽视了模型在开发、分发与部署过程中涉及多方参与的复杂供应链现实。本文系统梳理了在ML供应链中实现数据更正与删除权所面临的障碍。结合学术文献与数据保护机构指南，我们发现许多GDPR要求目前尚无法在实践中技术实现。研究进一步表明，现有工作对ML供应链中的问题关注不足。为此，我们引入‘模型在暗处’这一概念——即在ML链条下游产生的、缺乏透明度与可追溯性的衍生模型，并分析其带来的紧迫挑战。通过跨学科视角，本文旨在弥合法律要求与技术实现之间的差距，推动可信人工智能的发展。
原文摘要 · Abstract (English)
The rights to rectification and erasure, as established under the General Data Protection Regulation (GDPR), are central to protecting individuals' privacy. However, their effective enforcement in machine learning (ML) systems remains challenging. Existing work has largely addressed these rights from either a legal or a technical perspective in isolation and disregards the fact that models are produced in complex supply chains involving multiple actors across development, distribution, and deployment. This paper presents a holistic survey of challenges in implementing the rights to rectification and erasure in ML models. Drawing on academic literature and guidance from data protection authorities, we find that many GDPR requirements cannot yet be technically met in practice. Our findings further suggest that issues arising in ML supply chains are insufficiently addressed in research. To tackle this gap, we introduce the notion of models in the dark -- derived models created further downstream in an ML chain without sufficient transparency or traceability -- and analyse the urgent challenges posed by this phenomenon. By adopting an interdisciplinary perspective, this work contributes to bridging the gap between legal requirements and the technical implementation of data subject rights in ML, ultimately supporting the development of trustworthy artificial intelligence.
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