The measure and mismeasure of fairness: a critical review of fair machine learning
The measure and mismeasure of fairness: a critical review of fair machine learning, Corbett-Davies & Goel, arXiv 2018
With many thanks to Ben Fried and the ACM Queue editorial board for the paper recommendation.
We’ve visited the topic of fairness in the context of machine learning several times on The Morning Paper (see e.g. [1]1, [2]2, [3]3, [4]4). I’m still picking up new insights every time I revisit the topic though, and today’s paper choice is no exception.
In 1911 Russell & Whitehead published Principia Mathematica, with the goal of providing a solid foundation for all of mathematics. In 1931 Gödel’s Incompleteness Theorem shattered the dream, showing that for any consistent axiomatic system there will always be theorems that cannot be proven within the system. In case you’re wondering where on earth I’m going with this… it’s a very stretched analogy I’ve been playing with in my mind. One premise of many models of fairness in machine learning is that you can measure (‘prove’) fairness of a machine learning model from within the system – i.e. from properties of the Continue reading
