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Blind Justice: Fairness with Encrypted Sensitive Attributes

Kilbertus, N; Gascon, A; Kusner, M; Veale, M; Gummadi, K; Weller, A; (2018) Blind Justice: Fairness with Encrypted Sensitive Attributes. In: Dy, J and Krause, A, (eds.) Proceedings of the 35th International Conference on Machine Learning. (pp. pp. 2635-2644). International Machine Learning Society (IMLS).: Stockholm, Sweden. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Blind Justice: Fairness with Encrypted Sensitive Attributes
Event: The 35th International Conference on Machine Learning (ICML 2018)
Location: Stockholm, Sweden
Dates: 10 July 2018 - 15 July 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v80/kilbertus18a.html
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Laws
URI: https://discovery.ucl.ac.uk/id/eprint/10049934
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