Towards A Holistic View of Bias in Machine Learning: Bridging
Algorithmic Fairness and Imbalanced Learning
- URL: http://arxiv.org/abs/2207.06084v1
- Date: Wed, 13 Jul 2022 09:48:52 GMT
- Title: Towards A Holistic View of Bias in Machine Learning: Bridging
Algorithmic Fairness and Imbalanced Learning
- Authors: Damien Dablain, Bartosz Krawczyk, Nitesh Chawla
- Abstract summary: A key element in achieving algorithmic fairness with respect to protected groups is the simultaneous reduction of class and protected group imbalance in the underlying training data.
We propose a novel oversampling algorithm, Fair Oversampling, that addresses both skewed class distributions and protected features.
- Score: 8.602734307457387
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Machine learning (ML) is playing an increasingly important role in rendering
decisions that affect a broad range of groups in society. ML models inform
decisions in criminal justice, the extension of credit in banking, and the
hiring practices of corporations. This posits the requirement of model
fairness, which holds that automated decisions should be equitable with respect
to protected features (e.g., gender, race, or age) that are often
under-represented in the data. We postulate that this problem of
under-representation has a corollary to the problem of imbalanced data
learning. This class imbalance is often reflected in both classes and protected
features. For example, one class (those receiving credit) may be
over-represented with respect to another class (those not receiving credit) and
a particular group (females) may be under-represented with respect to another
group (males). A key element in achieving algorithmic fairness with respect to
protected groups is the simultaneous reduction of class and protected group
imbalance in the underlying training data, which facilitates increases in both
model accuracy and fairness. We discuss the importance of bridging imbalanced
learning and group fairness by showing how key concepts in these fields overlap
and complement each other; and propose a novel oversampling algorithm, Fair
Oversampling, that addresses both skewed class distributions and protected
features. Our method: (i) can be used as an efficient pre-processing algorithm
for standard ML algorithms to jointly address imbalance and group equity; and
(ii) can be combined with fairness-aware learning algorithms to improve their
robustness to varying levels of class imbalance. Additionally, we take a step
toward bridging the gap between fairness and imbalanced learning with a new
metric, Fair Utility, that combines balanced accuracy with fairness.
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