Algorithms that remember: model inversion attacks and data protection law

• Published in: Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 376; no. 2133; p. 20180083
• Main Authors: Veale, Michael, Binns, Reuben, Edwards, Lilian
• Format: Journal Article
• Language: English
• Published: England The Royal Society Publishing 15.10.2018
• Subjects: Machine Learning; Model Inversion; Model Trading; Personal Data; model inversion; machine learning; model trading; personal data
• ISSN: 1364-503X; 1471-2962; 1471-2962
• DOI: 10.1098/rsta.2018.0083

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'.