#25: MLDublin meets Nokia Bell Labs @ DogPatch Labs

Over the past few years, fairness has emerged as a matter of serious concern within machine learning. There is growing recognition that even models developed with the best of intentions may exhibit discriminatory biases, perpetuate inequality, or perform less well for historically disadvantaged groups. Considerable work is already underway within and outside machine learning to both characterize and address these problems. Following the leading researcher in the topic Barocas and Hardt we will parse the topic, adopting three perspectives: statistics, causality, and measurement. Rather than attempting to resolve questions of fairness within a single technical framework, the talk aims to intoroduce the audience with a coherent toolkit to critically examine the many ways that machine learning implicates fairness.