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Leveraging Biased AI Data in Medicine: Archeological Approach

Leveraging Biased AI Data in Medicine: Archeological Approach
miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.

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How an archeological approach can help leverage biased data in AI to improve medicine

An archeological view on biased data could help improve machine-learning models in health care settings. ....

Marzyeh Ghassemi , Elaine Nsoesie , Vinith Suriyakumar , Lawrence Tabak , Maxine Mackintosh , White House Office Of Science , Alan Turing Institute , International Conference On Machine , Artificial Intelligence Laboratory , Health Care , Institute Of Medical Engineering , Johns Hopkins University , Boston University Of Public Health , National Institutes Of Health , Kai Program , New England Journal , White House Office , Abdul Latif Jameel Clinic , Machine Learning , Jameel Clinic , Computer Science , Medical Engineering , Biased Data , Informative Artifacts , Assisted Health Care , Kadija Ferryman ,

How To Help High Schoolers Prepare for the Rise of Artificial Intelligence

How To Help High Schoolers Prepare for the Rise of Artificial Intelligence
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United States , Bunker Hill Community College , Troy High School , New York , Harvard University , Chelsea High School , Cornell University , Rachel Wu , Oliver Thiel , Joseph Bates , Antonella Catanzaro , Tiffany Pereira Portela , Noah Jones , Jeremy Wohlwend , Alex Ouyang , Rohan Kundargi , Vinith Suriyakumar , Carles Monterrubio , Maggie Wang , Jay Ananth , Pulkit Agrawal , Ignacio Fuentes , Florian Fintelmann , Collin Stultz , Tien Pham , Ciarra Brodie ,

Clip Scientific American Algorithms Are Making Important Decisions. What Could Possibly Go Wrong? (Sept. 7) | MIT News

Clip Scientific American Algorithms Are Making Important Decisions. What Could Possibly Go Wrong? (Sept. 7) | MIT News
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United States , Marzyeh Ghassemi ,

Algorithms Are Making Important Decisions. What Could Possibly Go Wrong?

Seemingly trivial differences in training data can skew the judgments of AI programs—and that’s not the only problem with automated decision-making ....

New Delhi , United States , Aparna Balagopalan , Angelina Wang , Marzyeh Ghassemi , Ali Alkhatib , World Bank , Massachusetts Institute Of Technology , Princeton University , University Of San Francisco , Human Rights Watch , Jordan National Aid Fund , Massachusetts Institute , Applied Data Ethics , San Francisco , Restricted Mode , National Aid Fund , Machine Learning ,