arXiv:2606.05946v1 Announce Type: new Abstract: 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 chal
The increasing deployment of ML systems and growing public and regulatory scrutiny on data privacy make the practical enforcement of GDPR principles in AI supply chains a critical and timely issue.
This highlights the growing challenge and necessity for ML developers and operators to integrate legal compliance, specifically GDPR rights, into complex, multi-actor AI supply chains, impacting design, deployment, and operational risk.
The paper shifts the focus from isolated legal or technical solutions to a holistic view of GDPR compliance across the entire ML supply chain, necessitating integrated approaches from all stakeholders.
- · Privacy-preserving AI solutions providers
- · Legal-tech platforms for AI compliance
- · Ethical AI consultants
- · Individuals with data rights
- · ML developers ignoring privacy-by-design
- · Companies with opaque AI supply chains
- · Data brokers mismanaging ML data
- · Organizations non-compliant with GDPR
Companies will need to invest more in auditable and transparent ML development processes to ensure GDPR compliance.
This will drive the development of new tooling and frameworks specifically designed to track and manage data lineage and model modifications across ML supply chains.
Increased accountability and enforcement actions could lead to a 'privacy-first' paradigm shift in ML system design, potentially slowing innovation in certain areas but increasing trust.
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