Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data

TL;DR

This paper introduces CFSMOTE, a novel pre-processing method for online learning that simultaneously addresses class imbalance and fairness concerns without optimizing a single fairness metric, improving group fairness metrics effectively.

Contribution

The paper presents CFSMOTE, a fairness-aware, continuous oversampling technique that mitigates bias and class imbalance simultaneously in stream learning, avoiding trade-offs of single-metric optimization.

Findings

Significant improvement in group fairness metrics over vanilla C-SMOTE

Maintains competitive predictive performance

Effective in balancing fairness and class imbalance in data streams

Abstract

As machine learning is increasingly applied in an online fashion to deal with evolving data streams, the fairness of these algorithms is a matter of growing ethical and legal concern. In many use cases, class imbalance in the data also needs to be dealt with to ensure predictive performance. Current fairness-aware stream learners typically attempt to solve these issues through in- or post-processing by focusing on optimizing one specific discrimination metric, addressing class imbalance in a separate processing step. While C-SMOTE is a highly effective model-agnostic pre-processing approach to mitigate class imbalance, as a side effect of this method, algorithmic bias is often introduced. Therefore, we propose CFSMOTE - a fairness-aware, continuous SMOTE variant - as a pre-processing approach to simultaneously address the class imbalance and fairness concerns by employing situation testing and balancing fairness-relevant groups during oversampling. Unlike other fairness-aware stream learners, CFSMOTE is not optimizing for only one specific fairness metric, therefore avoiding potentially problematic trade-offs. Our experiments show significant improvement on several common group fairness metrics in comparison to vanilla C-SMOTE while maintaining competitive performance, also in comparison to other fairness-aware algorithms.