Causal foundations of bias, disparity and fairness
This work addresses the need for precise definitions in bias and fairness research, which is crucial for researchers and policymakers in fields like AI and social sciences, though it is incremental in refining existing concepts.
The authors tackled the problem of ambiguous definitions of bias and disparity in social and behavioral sciences by proposing to define bias as an unjustified direct causal effect and disparity as a causal effect that includes bias, aiming to enable more rigorous and systematic study of these concepts.
The study of biases, such as gender or racial biases, is an important topic in the social and behavioural sciences. However, the literature does not always clearly define the concept. Definitions of bias are often ambiguous or not provided at all. To study biases in a precise manner, it is important to have a well-defined concept of bias. We propose to define bias as a direct causal effect that is unjustified. We propose to define the closely related concept of disparity as a direct or indirect causal effect that includes a bias. Our proposed definitions can be used to study biases and disparities in a more rigorous and systematic way. We compare our definitions of bias and disparity with various criteria of fairness introduced in the artificial intelligence literature. In addition, we discuss how our definitions relate to discrimination. We illustrate our definitions of bias and disparity in two case studies, focusing on gender bias in science and racial bias in police shootings. Our proposed definitions aim to contribute to a better appreciation of the causal intricacies of studies of biases and disparities. We hope that this will also promote an improved understanding of the policy implications of such studies.