A Principled Approach for Data Bias Mitigation

TL;DR

This paper introduces a mathematically grounded, explainable framework for mitigating intersectional data bias in machine learning datasets, capable of handling complex label types and multiple sensitive attributes.

Contribution

It proposes a novel bias mitigation strategy that leverages table discovery to add unbiased data tuples, with theoretical guarantees and applicability to complex, multi-attribute bias scenarios.

Findings

Effective bias reduction on public datasets

Mathematical guarantees of correctness

Insights into intersectional bias

Abstract

The widespread use of machine learning and data-driven algorithms for decision making has been steadily increasing over many years. \emph{Bias} in the data can adversely affect this decision-making. We present a new mitigation strategy to address data bias. Our methods are explainable and come with mathematical guarantees of correctness. They can take advantage of new work on table discovery to find new tuples that can be added to a dataset to create real datasets that are unbiased or less biased. Our framework covers data with non-binary labels and with multiple sensitive attributes. Hence, we are able to measure and mitigate bias that does not appear over a single attribute (or feature), but only intersectionally, when considering a combination of attributes. We evaluate our techniques on publicly available datasets and provide a theoretical analysis of our results, highlighting novel insights into data bias.