Lily Hu is a PhD candidate in Applied Mathematics and Philosophy at Harvard University. Her current project is on causal inference methodology in the social sciences and is interested in how various statistical frameworks treat and measure the “causal effect” of social categories such as race, and ultimately, how such methods are seen to back normative claims about racial discrimination and inequalities broadly. Previously, she has worked on topics in machine learning theory and algorithmic fairness.

September 25, 2020

Analysis

Direct Effects

How should we measure racial discrimination?

A 2018 National Academy of Sciences report on American policing begins its section on racial bias by noting the abundance of scholarship that records disparities in the criminal justice system. But shortly thereafter, the authors make a strange clarification: “In…

October 17, 2019

Analysis

Disparate Causes, pt. II

On the hunt for the correct counterfactual

An accurate understanding of the nature of race in our society is a prerequisite for an adequate normative theory of discrimination.

October 11, 2019

Analysis

Disparate Causes, pt. I

The shortcomings of causal and counterfactual thinking about racial discrimination

Legal claims of disparate impact discrimination go something like this: A company uses some system (e.g., hiring test, performance review, risk assessment tool) in a way that impacts people. Somebody sues, arguing that it has a disproportionate adverse effect on…