After Florent last week, this Wednesday, Ana (Patrón Piñerez) will give a talk to conclude her internship in Montréal (supervised with Agathe), on Bayesian Improved Surname Geocoding to predict “Race” in the U.S.

This study focuses on predicting race within the United States, a topic of significant sensitivity due to legal prohibitions against discrimination based on ‘race, color, or previous condition of servitude’ (Civil Rights Act of 1866, 1964). At the same time, due to the rising prevalence of big data systems, insurers are increasingly required to adhere to regulations such as Colorado SB21-169, which mandate demonstrating non-discriminatory practices toward sensitive attributes such as race. In this context, our research explores various methodologies for race prediction. We begin by examining pre-Bayesian approaches that utilize geolocation and surname data, progressing to Bayesian methods that integrate these sources. Specifically, we discuss Bayesian Improved Surname Geocoding (BISG) and its adaptations. Challenges associated with these techniques, such as handling zero counts for minorities and data scarcity, are also addressed. Finally, we propose a novel strategy known as nested dichotomies, rooted in the BISG algorithm. Unlike traditional multiclass prediction, this approach involves sequential binomial predictions structured within a nested framework.