POTS: Protective optimization technologies, Kulynych, Overdorf et al., arXiv 2019 With thanks to @TedOnPrivacy for recommending this paper via Twitter. Last time out we looked at fairness in the context of machine learning systems, coming to the realisation that you can't define 'fair' solely from the perspective of an algorithm and the data it is … Continue reading POTS: protective optimization technologies

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, … Continue reading The measure and mismeasure of fairness: a critical review of fair machine learning