Fair AutoML
- URL: http://arxiv.org/abs/2111.06495v1
- Date: Thu, 11 Nov 2021 23:08:34 GMT
- Title: Fair AutoML
- Authors: Qingyun Wu, Chi Wang
- Abstract summary: We present an end-to-end automated machine learning system to find machine learning models not only with good prediction accuracy but also fair.
System includes a strategy to dynamically decide when and on which models to conduct unfairness mitigation according to the prediction accuracy, fairness and the resource consumption on the fly.
- Score: 18.451193430486175
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We present an end-to-end automated machine learning system to find machine
learning models not only with good prediction accuracy but also fair. The
system is desirable for the following reasons. (1) Comparing to traditional
AutoML systems, this system incorporates fairness assessment and unfairness
mitigation organically, which makes it possible to quantify fairness of the
machine learning models tried and mitigate their unfairness when necessary. (2)
The system is designed to have a good anytime `fair' performance, such as
accuracy of a model satisfying necessary fairness constraints. To achieve it,
the system includes a strategy to dynamically decide when and on which models
to conduct unfairness mitigation according to the prediction accuracy, fairness
and the resource consumption on the fly. (3) The system is flexible to use. It
can be used together with most of the existing fairness metrics and unfairness
mitigation methods.
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