  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.
