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Empirical Risk Minimization Under Fairness Constraints

Donini, M; Oneto, L; Ben-David, S; Shawe-Taylor, J; Pontil, M; (2018) Empirical Risk Minimization Under Fairness Constraints. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.) Proceedings of the 32nd Conference on Neural Information Processing Systems. Neural Information Processing Systems (NIPS): Montreal, Canada. Green open access

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Abstract

We address the problem of algorithmic fairness: ensuring that sensitive information does not unfairly influence the outcome of a classifier. We present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem. It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable. We derive both risk and fairness bounds that support the statistical consistency of our methodology. We specify our approach to kernel methods and observe that the fairness requirement implies an orthogonality constraint which can be easily added to these methods. We further observe that for linear models the constraint translates into a simple data preprocessing step. Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches.

Type: Proceedings paper
Title: Empirical Risk Minimization Under Fairness Constraints
Event: 32nd Conference on Neural Information Processing Systems (Neuro IPS 2018)
Location: Montreal, CANADA
Dates: 02 December 2018 - 08 December 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: https://papers.nips.cc/paper/7544-empirical-risk-m...
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
URI: https://discovery.ucl.ac.uk/id/eprint/10073434
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