Algorithms that remember: model inversion attacks and data protection law

TLDR

Many individuals are concerned about the governance of machine‑learning systems and the prevention of algorithmic harms, and the EU’s GDPR is seen as a core tool for better governance, though it mainly governs personal data while models are treated as intellectual property. The paper investigates whether model inversion and membership inference attacks can cause machine‑learned models to be legally classified as personal data and explores the resulting rights and obligations. The authors review recent information‑security literature on these attacks and conduct a probing experiment to assess the legal implications and suggest future governance directions. This article is part of the theme issue “Governing artificial intelligence: ethical, legal, and technical opportunities and challenges.”.

Abstract

Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around ‘model inversion’ and ‘membership inference’ attacks, which indicates that the process of turning training data into machine-learned systems is not one way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.