Algorithmic Factors Influencing Bias in Machine Learning

TL;DR

This paper investigates how algorithmic factors such as regularization and data imbalance can cause bias in machine learning models, and proposes counterfactual management to reduce this bias.

Contribution

It reveals how ML algorithms can misrepresent training data through underestimation and offers a method using synthetic counterfactuals to mitigate this bias.

Findings

Underestimation contributes to bias in ML models.

Regularization and data imbalance exacerbate underestimation.

Counterfactual management reduces bias effectively.

Abstract

It is fair to say that many of the prominent examples of bias in Machine Learning (ML) arise from bias that is there in the training data. In fact, some would argue that supervised ML algorithms cannot be biased, they reflect the data on which they are trained. In this paper we demonstrate how ML algorithms can misrepresent the training data through underestimation. We show how irreducible error, regularization and feature and class imbalance can contribute to this underestimation. The paper concludes with a demonstration of how the careful management of synthetic counterfactuals can ameliorate the impact of this underestimation bias.