Data for good talk at Columbia Data Science Institute

(Note: links don’t work in this preview, click on the post to view). I’m happy to be speaking at 1pm EST today at Columbia University on the topics of causal inference and selection bias in algorithmic fairness. I believe video will be available at the webinar link, and here are my slides. The talk is based on work described in this survey with my coauthors Matt Kusner, Chris Russell, and Ricardo Silva. See here for the video for Matt’s oral presentation of our first paper in this line of work at NIPS 2017. [Read More]

Algorithmic fairness is as hard as causation

This post describes a simple example that illustrates why algorithmic fairness is a hard problem. I claim it is at least as hard as doing causal inference from observational data, i.e. distinguishing between mere association and actual causation. In the process, we will also see that SCOTUS Chief Justice Roberts has a mathematically incorrect theory on how to stop discrimination. Unfortunately, that theory persists as one of the most common constraints on fairness. [Read More]