Differential Expressiveness: A Data-Centered Perspective on Algorithmic Bias

Abstract

The interdisciplinary study of algorithmic fairness and bias has enjoyed a meteoric rise in popularity over the past several years, motivated in no small part by the increasingly influential impact of machine learning in many aspects of daily life. One part of this field examines the foundational issue of bias being present in the training data that is provided to an algorithm, seeking to develop ways to describe and mitigate this issue.

We propose a new and broad characterization of a kind of data bias that we call differential expressiveness (DE). We formulate DE as being quality of an individual feature in a dataset, conveying a condition where the values of the feature cannot be consistently interpreted across different individuals. Contextualizing our presentation with an overview of the development of algorithmic fairness, we give two mathematical interpretations of DE and explore how the interpretations relate to one another. In addition, we discuss a variety of case studies illustrating how we can use DE to interpret data bias in real-world examples. Finally, we explore how DE complements existing frameworks in the literature for modelling data bias.