The ability to distinguish between people in setting the price of credit is often constrained by legal rules that aim to prevent discrimination. These legal requirements have developed focusing on human decision-making contexts, and so their effectiveness is challenged as pricing increasingly relies on intelligent algorithms that extract information from big data. In this Essay, we bring together existing legal requirements with the structure of machine-learning decision-making in order to identify tensions between old law and new methods and lay the ground for legal solutions. We argue that, while automated pricing rules provide increased transparency, their complexity also limits the application of existing law. Using a simulation exercise based on real-world mortgage data to illustrate our arguments, we note that restricting the characteristics that the algorithmis allowed to use can have a limited effect on disparity and can in fact increase pricing gaps. Furthermore, we argue that there are limits to interpreting the pricing rules set by machine learning that hinders the application of existing discrimination laws. We end by discussing a framework for testing discrimination that evaluates algorithmic pricing rules in a controlled environment. Unlike the human decision-making context, this framework allows for ex ante testing of price rules, facilitating comparisons between lenders.
Banking and Finance Law | Civil Rights and Discrimination | Law
Talia B. Gillis & Jan L. Speiss,
Big Data and Discrimination,
U. Chi. L. Rev.
Available at: https://scholarship.law.columbia.edu/faculty_scholarship/2983