Reviews: Equality of Opportunity in Supervised Learning

It treats an incredibly important and foundational problem (fairness), proposes a creative but simple new definition, gives techniques for achieving the definition, proves theorems with regards to optimality, and even provides empirical results. As learning algorithms are used more and more broadly in situations where their decisions affect people's lives, fairness of these algorithms becomes a critical technical, social, and legal problem. While there is certainly no single "right" definition and paradigm when it comes to fairness, this definition seems to clearly be *a* right definition. It's so clean and simple that in retrospect, it seems obvious--a sign of an excellent idea. One of the many things I love about this definition and this work is how it shifts the structure of power and incentives--once a learner is constrained to be fair, under either of the definitions proposed, she is immediately incentivised to gather more data or make other efforts to do a better job of understanding protected populations.