fairness_lupi.pdf (19.19 MB)
Recycling privileged learning and distribution matching for fairness
conference contribution
posted on 2023-06-09, 08:44 authored by Novi QuadriantoNovi Quadrianto, Viktoriia SharmanskaViktoriia SharmanskaEquipping machine learning models with ethical and legal constraints is a serious issue; without this, the future of machine learning is at risk. This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions. In legal scholarships, the notion of fairness itself is evolving and multi-faceted. We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future. To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions. We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness. Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space. We show several existing models are special cases of ours. Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions. This will facilitate decision makers to select an operating point and to be accountable for it.
Funding
EthicalML: Injecting Ethical and Legal Constraints into Machine Learning Models; G2034; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL
History
Publication status
- Published
File Version
- Accepted version
Journal
Advances in Neural Information Processing Systems 30 (NIPS 2017)Publisher
Neural Information Processing Systems FoundationPublisher URL
Page range
1-12Event name
31st Annual Conference on Neural Information Processing SystemsEvent location
Long Beach, California, USEvent type
conferenceEvent date
4-9 December 2017Place of publication
Red Hook, NYDepartment affiliated with
- Informatics Publications
Research groups affiliated with
- Data Science Research Group Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-11-08First Open Access (FOA) Date
2017-11-09First Compliant Deposit (FCD) Date
2017-11-08Usage metrics
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