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Researchers challenge long-held assumption about ML trade-offs in public policy decisions

Carnegie Mellon University researchers are challenging a long-held assumption that there is a trade-off between accuracy and fairness when using machine learning to make public policy decisions.

Hemank lambaRayid ghaniEmily hendersonCarnegie mellon universitySchool of computer scienceMachine learning departmentSchool of computer science machine learning departmentHeinz college of information systemsMellon universityNature machineComputer scienceHeinz collegeInformation systemsPublic policyKit rodolfaCarnegie mellon

$125M in Grants to Study Cellular Aging to Be Coordinated from Pittsburgh

Oct. 21, 2021 Grants totaling as much as $125 million over five years to 16 institutions across the U.S. will enable scientists to explore the human

Jonathan silversteinDepartment of biomedical informaticsResearch informaticsNational cancer instituteNational institute onTechnology developmentPhil blood of the pittsburgh supercomputing centerCarnegie mellon university school of computer scienceUniversity of pittsburghInstitute for precision medicinePitt school of medicineCommon fundHealth sciencesData coordinating centerComputational biology departmentCellular senescence network sennet

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