Algorithmic Factors Influencing Bias in Machine Learning
This addresses bias issues in ML for practitioners, but it is incremental as it builds on known factors like data bias.
The paper tackles the problem of bias in machine learning by demonstrating how algorithms can misrepresent training data through underestimation, and shows that managing synthetic counterfactuals can reduce this bias.
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.