Title: Limits and opportunities in algorithmic fairness
Abstract: In this talk I will survey some recent literature on algorithmic fairness with a focus on methods based on causal inference. For example, the basic idea of counterfactual fairness is that a decision should be the same both in the actual world and in a counterfactual world where an individual had a different value of a sensitive attribute, such as race or gender. This approach defines fairness in the context of a causal model for the data, and can be used to understand the limitations and implicit assumptions or consequences of other definitions. I will conclude by highlighting some hard problems in this area, and suggesting a few simple heuristics to guide future progress.
Short bio: Joshua Loftus is an Assistant Professor of Statistics at NYU Stern School of Business. Before joining NYU, he earned his PhD at Stanford University and was a Research Fellow at the Alan Turing Institute. His research interests include statistical methods for inference after model selection and the application of causal methods to algorithmic fairness.