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Why It's Hard to Design Fair Machine Learning Models
Manage episode 217702587 series 1427720
O'Reilly Radar에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 O'Reilly Radar 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.
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