Seminar by Arthur Charpentier

Please Note: This seminar will be given in person.

Causal Inference and Counterfactuals with Optimal Transport With Applications in Fairness and Discrimination

The analysis of discrimination has long interested economists and lawyers. In recent years, the literature in computer science and machine learning has become interested in the subject, offering an interesting re-reading of the topic. These questions are the consequences of numerous criticisms of algorithms used to translate texts or to identify people in images. With the arrival of massive data, and the use of increasingly opaque algorithms, it is not surprising to have discriminatory algorithms, because it has become easy to have a proxy of a sensitive variable, by enriching the data indefinitely. According to Kranzberg (1986), "technology is neither good nor bad, nor is it neutral", and therefore, "machine learning won't give you anything like gender neutrality `for free' that you didn't explicitely ask for", as claimed by Kearns et a. (2019). In this talk, we will focus on the use of Optimal Transport first to quantify the risk and derive a counterfactual version of a policyholder, and then to mitigate a potential discrimination.