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Oral
Blind Justice: Fairness with Encrypted Sensitive Attributes
Niki Kilbertus · Adria Gascon · Matt Kusner · Michael Veale · Krishna Gummadi · Adrian Weller

Fri Jul 13 01:20 AM -- 01:30 AM (PDT) @ A6
in Society Impacts of Machine Learning 2 »

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.

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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

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. 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.

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