Dec 14, 2019
The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last few years, several formal definitions of fairness have gained prominence. But, in this talk, I'll argue that nearly all of these popular mathematical formalizations suffer from significant statistical limitations. In particular, when used as design objectives, these definitions, perversely, can harm the very groups they were intended to protect.
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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