Technology & Power, Part 3: Machine Learning and Power episode artwork

EPISODE · Dec 6, 2021 · 59 MIN

Technology & Power, Part 3: Machine Learning and Power

from The ThinkND Podcast · host ThinkND - University of Notre Dame

Episode Topic: Machine Learning and PowerMachine learning purports to make accurate predictions and decisions about everything from prison recidivism rates to cancer diagnoses to mortgage approvals. But time and time again, scholars, activists, and journalists have demonstrated that machine learning algorithms often digitize and replicate inaccuracies, historical prejudices, and institutional harms. With so much on the line, machine learning models and the data used to train them may deserve more scrutiny.Featured Speakers: Abeba Birhane, PhD Candidate in Cognitive Science, University College DublinNoopur Raval, Postdoctoral Researcher, AI Now InstituteRead this episode's recap over on the University of Notre Dame's open online learning community platform, ThinkND: go.nd.edu/b85c59. This podcast is a part of the TEC Talks ThinkND Series titled “Technology & Power”.Thanks for listening! The ThinkND Podcast is brought to you by ThinkND, the University of Notre Dame's online learning community. We connect you with videos, podcasts, articles, courses, and other resources to inspire minds and spark conversations on topics that matter to you — everything from faith and politics, to science, technology, and your career.Learn more about ThinkND and register for upcoming live events at think.nd.edu.Join our LinkedIn community for updates, episode clips, and more.

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Episode Topic: Machine Learning and Power Machine learning purports to make accurate predictions and decisions about everything from prison recidivism rates to cancer diagnoses to mortgage approvals. But time and time again, scholars, activists, and journalists have demonstrated that machine learning algorithms often digitize and replicate inaccuracies, historical prejudices, and institutional harms. With so much on the line, machine learning models and the data used to train them may deserve...

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Technology & Power, Part 3: Machine Learning and Power

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