Can AI foresee someone's breakup? - Thomas Hofweber

Explore one of the main issues of AI-based predictive models and how it affects user decision-making and accountability.--A couple has been considering getting engaged, but they’re worried about divorce statistics. An AI-based model was just released that can predict your likelihood of divorce with 95% accuracy. The only catch is the model doesn’t offer any reasons for its results. So, should they decide whether or not to get married based on this AI’s prediction? Thomas Hofweber explores AI’s transparency problem.Lesson by Thomas Hofweber, directed by Hannah Lau-Walker.This video was produced in collaboration with the Parr Center for Ethics, housed within the renowned Philosophy Department at the University of North Carolina at Chapel Hill. The Parr Center is committed to integrating abstract work in ethical theory with the informed discussion of practical ethical issues, and prides itself on the development of innovative and inclusive approaches to moral and civic education.Support Our Non-Profit Mission----------------------------------------------Support us on Patreon: http://bit.ly/TEDEdPatreonCheck out our merch: http://bit.ly/TEDEDShop----------------------------------------------Connect With Us----------------------------------------------Sign up for our newsletter: http://bit.ly/TEDEdNewsletterFollow us on Facebook: http://bit.ly/TEDEdFacebookFind us on Twitter: http://bit.ly/TEDEdTwitterPeep us on Instagram: http://bit.ly/TEDEdInstagram----------------------------------------------Keep Learning----------------------------------------------View full lesson: https://ed.ted.com/lessons/would-you-use-a-machine-that-predicts-your-future-thomas-hofweberDig deeper with additional resources: https://ed.ted.com/lessons/would-you-use-a-machine-that-predicts-your-future-thomas-hofweber/digdeeperAnimator's website: https://www.hannahlauwalker.com----------------------------------------------Thank you so much to our patrons for your support! Without you this video would not be possible! Jesús Bíquez Talayero, Chels Raknrl, Sai Pranavi Jonnalagadda, Stuart Rice, Jing Chen, Vector-Dopamine math, Jasper Song, Giorgio Bugnatelli, Chardon, Eddy Trochez, OnlineBookClub.org, Eric Shear, Leith Salem, Omar Hicham, Adrian Rotaru, Brad Sullivan, Karen Ho, Niklas Frimberger, Hunter Manhart, Nathan Nguyen, Igor Stavchanskiy, James R DeVries, Grace Huo, Diana Huang, Chau Hong Diem, Orlellys Torre, Corheu, Thomas Mee, Maryann H McCrory, Blas Borde, John Hellmann, Poompak Meephian, Chuck Wofford, Adam Pagan, Wes Winn, Conder Shou, ntiger, Noname, Hansan Hu, David D, Mac Hyney, Keith Ellison, robin valero walters, Lynne Truesdale, Gatsby Dkdc, Matthew Neal, Denis Chon, Julian Oberhofer, Monte Carroll and Eddy. Learn more about your ad choices. Visit megaphone.fm/adchoices