- 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 in...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 463.3KB)
-
- Publisher copy:
- 10.1098/rsta.2018.0083
-
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- Funding agency for:
- Binns, RDP
- Publisher:
- Royal Society Publisher's website
- Journal:
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Journal website
- Volume:
- 376
- Issue:
- 2133
- Pages:
- Article: 20180083
- Publication date:
- 2018-10-15
- Acceptance date:
- 2018-07-12
- DOI:
- EISSN:
-
1471-2962
- ISSN:
-
1364-503X
- Pubs id:
-
pubs:935375
- URN:
-
uri:461fa4e2-226d-4341-99ba-3640f0bd38d8
- UUID:
-
uuid:461fa4e2-226d-4341-99ba-3640f0bd38d8
- Local pid:
- pubs:935375
- Copyright holder:
- Veale et al.
- Copyright date:
- 2018
- Notes:
-
Copyright © 2018 The Authors.
Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Journal article
Algorithms that remember: model inversion attacks and data protection law
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+ Engineering and Physical Sciences Research Council
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