Can AI Developers Be Incentivized to Debias their Algorithms?

The prevalence and technical relevance of machine learning algorithms have increased over the years, making predictive decision-making tools part of the everyday lives of online users. Today, it is harder to discern what decisions are made by humans, and the others that rely upon the cognition of machines. On this new episode of the Tech Tank podcast, Darrell West is joined by Nicol Turner Lee, Senior Fellow and the Director of the Center for Technology Innovation who explores the need for a proposed Energy Star rating, or incentive-based rating system to improve the performance and optimization of these online tools.   Hosted on Acast. See acast.com/privacy for more information.

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TechTank is a biweekly podcast from The Brookings Institution exploring the most consequential technology issues of our time. From artificial intelligence and racial bias in algorithms, to Big Tech, the future of work, and the digital divide, TechTank takes abstract ideas and makes them accessible. Moderators Dr. Nicol Turner Lee and Darrell West speak with leading technology experts and policymakers to share new data, ideas, and policy solutions to address the challenges of our new digital world.

 

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Hosted on Acast. See acast.com/privacy for more information.