Survey on Fairness Notions and Related Tensions
- URL: http://arxiv.org/abs/2209.13012v2
- Date: Mon, 19 Jun 2023 11:19:52 GMT
- Title: Survey on Fairness Notions and Related Tensions
- Authors: Guilherme Alves, Fabien Bernier, Miguel Couceiro, Karima Makhlouf,
Catuscia Palamidessi, Sami Zhioua
- Abstract summary: Automated decision systems are increasingly used to take consequential decisions in problems such as job hiring and loan granting.
However, objective machine learning (ML) algorithms are prone to bias, which results in yet unfair decisions.
This paper surveys the commonly used fairness notions and discusses the tensions among them with privacy and accuracy.
- Score: 4.257210316104905
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Automated decision systems are increasingly used to take consequential
decisions in problems such as job hiring and loan granting with the hope of
replacing subjective human decisions with objective machine learning (ML)
algorithms. However, ML-based decision systems are prone to bias, which results
in yet unfair decisions. Several notions of fairness have been defined in the
literature to capture the different subtleties of this ethical and social
concept (e.g., statistical parity, equal opportunity, etc.). Fairness
requirements to be satisfied while learning models created several types of
tensions among the different notions of fairness and other desirable properties
such as privacy and classification accuracy. This paper surveys the commonly
used fairness notions and discusses the tensions among them with privacy and
accuracy. Different methods to address the fairness-accuracy trade-off
(classified into four approaches, namely, pre-processing, in-processing,
post-processing, and hybrid) are reviewed. The survey is consolidated with
experimental analysis carried out on fairness benchmark datasets to illustrate
the relationship between fairness measures and accuracy in real-world
scenarios.
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