Machine learning has taken over our world, in more ways than we realize. There is also some work on fairness in machine learning in other settings—for example, ranking, 12 selection, 42,47 personalization, 13 bandit learning, 34,50 human-classifier hybrid decision systems, 53 and reinforcement learning. Codemotion and Facebook organized the Tech Leadership Training boot camp, heres a personal reportage from one of our attendees. Machine Learning Developer Machine learning and fairness, how to make it happen. AI and Machine Learning are front and center in the news on a daily basis. In conjunction with the release of this new Fairness module, we’ve added more than a dozen new fairness entries to our Machine Learning Glossary (tagged with a scale icon in the right margin). What we mean by fairness.

May 30, 2019 by Valerio Bernardi

Fighting against unfairness and discrimination has a long history in philosophy and psychology, and recently in machine learning. A Course on Fairness, Accountability and Transparency in Machine Learning Sponsored by the GIAN program of the Government of India View on GitHub Download .zip Download .tar.gz Overview. Fairness in ML Systems. 3. I provide an overview of some of this debate and nish with implications for the incorporation of ‘fairness’ into … We've visited the topic of fairness in the context of machine learning several times on The Morning Paper (see e.g. Goal: To assess and mitigate potential unfairness of machine learning (ML) systems..

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18,32 But outside of classification, the literature is relatively sparse.

Fairness in Machine Learning: Lessons from Political Philosophy while egalitarianism is a widely held principle, ex-actly what it requires is the subject of much de-bate. Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as a Jupyter widget … 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.