Causal foundations of bias, disparity and fairness

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.

Musk Promised Transparency, Then Hid Twitter Data

The CEO has changed a long-standing practice of sharing data with researchers who study how information spreads online. 

Premio Lagrange-Fondazione CRT - Fondazione CRT

La vincitrice è Tina Eliassi-Rad, professoressa di Computer Science alla Northeastern University di Boston, membro dell’Institute for Experiential AI e docente esterno presso il Santa Fe Institute.

IC2S2'23 Copenhagen

The Science of the Predicted Human talk series: Professor César A. Hidalgo

We are delighted to host Professor César A. Hidalgo (Universities of Toulouse, Manchester, and Harvard) in collaboration with DTU Compute and the Pioneer Centre for Artificial Intelligence