Fairness and machine learning

Fairness and machine learning Limitations and Opportunities EmphBox. This online textbook is an incomplete work in progress. Essential chapters are still missing. In the spirit of open review, we solicit broad feedback that will influence existing chapters, as well as the development of later material. Table of contents Introduction Classification We introduce formal non-discrimination criteria, establish their relationships, and illustrate their limitations. Legal background and normati

3 mentions: @mrtz@lavanyaai@James_Brusseau
Date: 2019/09/12 21:48

Referring Tweets

@mrtz We're excited to release a draft of the causality chapter at https://t.co/og0iNDglC0. There's been lots of interest in the topic and we hope the chapter is a helpful introduction. https://t.co/NuKYccTlBk
@James_Brusseau A sincere, inviting, generous vision of CS/Stat experts wrestling with the idea of ethics and the concept of fairness in AI/BigData/ML. https://t.co/cpCSoxWa0O #ai #bigdata #aiethics #dataethics
@lavanyaai I highly encourage y'all to follow @mrtz's work on fairness in machine learning!👼🏼 The authors have really interesting things to say about the ethical challenges of deploying ML systems, fairness as a founding principle rather than an afterthought etc. https://t.co/GdD5lpVk3s

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