Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact, sensitive attributes must be examined, e.g., in order to learn a fair model, or to check if a given model is fair. We introduce methods from secure multi-party computation which allow us to avoid both. By encrypting sensitive attributes, we show how an outcome-based fair model may be learned, checked, or have its outputs verified and held to account, without users revealing their sensitive attributes.
Author Information
Niki Kilbertus (MPI Tübingen & Cambridge)
Adria Gascon (The Alan Turing Institute / Warwick University)
Matt Kusner (Alan Turing Institute)
Michael Veale (UCL)
Krishna Gummadi (MPI-SWS)
Adrian Weller (University of Cambridge, Alan Turing Institute)
Adrian Weller is a Senior Research Fellow in the Machine Learning Group at the University of Cambridge, a Faculty Fellow at the Alan Turing Institute for data science and an Executive Fellow at the Leverhulme Centre for the Future of Intelligence (CFI). He is very interested in all aspects of artificial intelligence, its commercial applications and how it may be used to benefit society. At the CFI, he leads their project on Trust and Transparency. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Blind Justice: Fairness with Encrypted Sensitive Attributes »
Fri Jul 13th 10:20 -- 10:30 AM Room A6
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