Episode 74: How to Avoid Bias in Your Machine Learning Models with Clare Corthell

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This is a podcast episode titled, Episode 74: How to Avoid Bias in Your Machine Learning Models with Clare Corthell. The summary for this episode is: Bias exists everywhere. It factors into everything that we do and into virtually every decision that we make. An interesting, but problematic side effect of this is that bias can also easily slip into our machine models. In this episode, Jon Prial talks with Clare Corthell, a well-known and respected data scientist and engineer, and the founder of Luminant Data, about the issues that can arise when bias enters your models and how to avoid it in the first place. Plus, check out our show notes to find out how to access our first episode of Extra Impact, where Jon and Clare go deeper into this topic, talking about bias in AI-powered services like Airbnb and the controversial policing stop-and-frisk program. You’ll hear about: -- The nature of bias in machine learning models and what causes it -- Cynthia Dwork’s work on transparency -- How companies should be thinking about data -- Implementing fairness into AI and machine learning models -- Developing an AI code of ethics Access the show notes here: http://bit.ly/2GLyjsU

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Bias exists everywhere. It factors into everything that we do and into virtually every decision that we make. An interesting, but problematic side effect of this is that bias can also easily slip into our machine models. In this episode, Jon Prial talks with Clare Corthell, a well-known and respected data scientist and engineer, and the founder of Luminant Data, about the issues that can arise when bias enters your models and how to avoid it in the first place. Plus, check out our show notes to find out how to access our first episode of Extra Impact, where Jon and Clare go deeper into this topic, talking about bias in AI-powered services like Airbnb and the controversial policing stop-and-frisk program. You’ll hear about: -- The nature of bias in machine learning models and what causes it -- Cynthia Dwork’s work on transparency -- How companies should be thinking about data -- Implementing fairness into AI and machine learning models -- Developing an AI code of ethics Access the show notes here: http://bit.ly/2GLyjsU