Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains

• URL: http://arxiv.org/abs/2606.05946v1
• Date: Thu, 04 Jun 2026 09:46:32 GMT
• Title: Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains
• Abstract summary: This paper presents challenges in implementing rights to rectification and erasure in machine learning models.<n>We find that many requirements from data protection authorities cannot yet be technically met in practice.<n>By adopting an interdisciplinary perspective, this work contributes to bridging the gap between legal requirements and the technical implementation of subject data rights in ML.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

Related papers

• On Automated and Explainable Provenance of AI-Generated Code [56.818202904805446]
  We argue that the next generation of CodeGenAI tools must be built on a foundation of explainable provenance.<n>We describe a research program combining large-scale empirical studies with post-hoc causal and interpretability techniques.
  arXiv  Detail & Related papers  (2026-08-03T14:50:31Z)
• Copyright in AI Pre-Training Data Filtering: Regulatory Landscape and Mitigation Strategies [0.0]
  The rapid advancement of general-purpose AI models has increased concerns about copyright infringement in training data.<n>This paper examines the regulatory landscape of AI training data governance in major jurisdictions, including the EU, the United States, and the Asia-Pacific region.<n>It also identifies critical gaps in enforcement mechanisms that threaten both creator rights and the sustainability of AI development.
  arXiv  Detail & Related papers  (2025-11-26T14:02:45Z)
• Global AI Governance Overview: Understanding Regulatory Requirements Across Global Jurisdictions [0.0]
  The rapid advancement of general-purpose AI models has increased concerns about copyright infringement in training data.<n>This paper examines the regulatory landscape of AI training data governance in major jurisdictions, including the EU, the United States, and the Asia-Pacific region.<n>It also identifies critical gaps in enforcement mechanisms that threaten both creator rights and the sustainability of AI development.
  arXiv  Detail & Related papers  (2025-11-26T13:59:11Z)
• SoK: Large Language Model Copyright Auditing via Fingerprinting [69.14570598973195]
  We introduce a unified framework and formal taxonomy that categorizes existing methods into white-box and black-box approaches.<n>We propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios.
  arXiv  Detail & Related papers  (2025-08-27T12:56:57Z)
• A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives [65.3369988566853]
  Recent studies have demonstrated that adversaries can replicate a target model's functionality.<n>Model Extraction Attacks pose threats to intellectual property, privacy, and system security.<n>We propose a novel taxonomy that classifies MEAs according to attack mechanisms, defense approaches, and computing environments.
  arXiv  Detail & Related papers  (2025-08-20T19:49:59Z)
• Rethinking Data Protection in the (Generative) Artificial Intelligence Era [138.07763415496288]
  We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
  arXiv  Detail & Related papers  (2025-07-03T02:45:51Z)
• Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models [0.0]
  We introduce a five-stage interdisciplinary framework that integrates legal and ML-technical analysis during machine learning model development.<n>This framework facilitates designing ML models in a legally aligned way and identifying high-performing models that are legally justifiable.
  arXiv  Detail & Related papers  (2025-04-23T13:41:17Z)
• OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution [35.68672391812135]
  We introduce OML, a primitive that enables a new distribution paradigm for AI models.<n>OML can be freely distributed for local execution while maintaining cryptographically enforced usage authorization.<n>This work opens a new research direction at the intersection of cryptography, machine learning, and mechanism design.
  arXiv  Detail & Related papers  (2024-11-01T18:46:03Z)
• AI Safety in Generative AI Large Language Models: A Survey [14.737084887928408]
  Large Language Model (LLMs) that exhibit generative AI capabilities are facing accelerated adoption and innovation. Generative AI (GAI) inevitably raises concerns about the risks and safety associated with these models. This article provides an up-to-date survey of recent trends in AI safety research of GAI-LLMs from a computer scientist's perspective.
  arXiv  Detail & Related papers  (2024-07-06T09:00:18Z)
• TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs [50.259001311894295]
  We propose a novel TRansformer-based Attribution framework using Contrastive Embeddings called TRACE. We show that TRACE significantly improves the ability to attribute sources accurately, making it a valuable tool for enhancing the reliability and trustworthiness of large language models.
  arXiv  Detail & Related papers  (2024-07-06T07:19:30Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.