It’s time for our annual predictions episode! Kara and Scott share their 2024 predictions on politics, stocks, China, Google and more. Plus, some Friend of Pivot predictions from Jen Psaki, Mike Birbiglia, Fei-Fei Li, Bill Cohan, Dr. Joy Buolamwini, and Matt Belloni.
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Kara shares her latest reporting on Sam Altman and his decision to go to Microsoft, then she and Scott discuss what's next for OpenAI. Plus, Elon Musk threatens a "thermonuclear lawsuit," and X CEO Linda Yaccarino resists calls to resign. Our Friend of Pivot is Dr. Joy Buolamwini, founder of the Algorithmic Justice League, and author of "Unmasking AI: My Mission to Protect What Is Human in a World of Machines." Dr. Joy gives her take on OpenAI and the Altman ouster, and also discusses her mission to root out bias in AI.Follow Joy at @jovialjoy
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Follow us on TikTok at @pivotpodcast.
Send us your questions by calling us at 855-51-PIVOT, or at nymag.com/pivot.
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Coded bias, intersectionality in AI, and computer vision: Founder of the Algorithmic Justice League Joy Buolamwini talks to host Jon Krohn about the impact of exclusion and inclusion in datasets, the need to address intersectionality when identifying racial, age, or gender-based prejudice in machine learning tools, protections for artists and creative practitioners against AI, and the role that AI may have in combating systemic racism.
This episode is brought to you by Gurobi, the Decision Intelligence Leader, and by CloudWolf, the Cloud Skills platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
In this episode you will learn:
• What coded bias is [06:49]
• The problem with bias in machine learning datasets [18:41]
• The Incoding Movement [42:08]
• About the Pilot Parliaments Benchmark [52:07]
• Ethics and the future of AI [1:20:10]
• The potential for AI to end systemic racism [1:32:59]
Additional materials: www.superdatascience.com/727
MIT grad student Joy Buolamwini was working with facial analysis software when she noticed a problem: the software didn't detect her face -- because the people who coded the algorithm hadn't taught it to identify a broad range of skin tones and facial structures. Now she's on a mission to fight bias in machine learning, a phenomenon she calls the "coded gaze." It's an eye-opening talk about the need for accountability in coding ... as algorithms take over more and more aspects of our lives.
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