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Author (up) Veale, M.; Binns, R.; Edwards, L. url  openurl
  Title Algorithms That Remember: Model Inversion Attacks and Data Protection Law Type Journal Article
  Year 2018 Publication Philosophical Transactions of the Royal Society a: Mathematical, Physical and Engineering Sciences Abbreviated Journal  
  Volume 376 Issue 2133 Pages 20180083  
  Keywords artificial intelligence, AI  
  Abstract Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EUs 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.  
  Address  
  Corporate Author Thesis  
  Publisher The Royal Society Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1364-503x ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number refbase @ admin @ veale_algorithms_2018 Serial 17413  
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