Speaker: Joshua Loftus, Assistant Professor of Information, Operations and Management Sciences, NYU Stern School of Business
In this talk, Professor Loftus 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.
This event is part of the “Data for Good” speaker series at the Columbia University Data Science Institute, in which distinguished speakers will grapple with the challenge of ensuring data science serves the public good. They will address such subjects as financial systems risk, interpretability and discrimination in machine learning, and different definitions of fairness and privacy.
This event is free and open to the public; No registration is required. Please visit the event webpage for more information.
