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FAIRNESS IN PRACTICE: DEFINING AND IMPLEMENTING DIVERSITY & REPRESENTATION IN AI DATASETS FOR HEALTHCARE - THERE IS WIDESPREAD AGREEMENT THAT FAIRNESS—ENSURING EQUITABLE PERFORMANCE ACROSS SIMILARLY SITUATED INDIVIDUALS

USAspending · observation · Publication date unknown

FAIRNESS IN PRACTICE: DEFINING AND IMPLEMENTING DIVERSITY & REPRESENTATION IN AI DATASETS FOR HEALTHCARE - THERE IS WIDESPREAD AGREEMENT THAT FAIRNESS—ENSURING EQUITABLE PERFORMANCE ACROSS SIMILARLY SITUATED INDIVIDUALS AND ACROSS GROUPS—IS A FUNDAMENTAL PRINCIPLE FOR ETHICAL DEVELOPMENT OF ARTIFICIAL INTELLIGENCE FOR HEALTH CARE (AI-HC). IN THE U.S., THE NIH HAS LAUNCHED INITIATIVES TO ADDRESS UNFAIRNESS DUE TO LACK OF DIVERSITY IN DATASETS, SUCH AS THE ALL OF US RESEARCH PROGRAM AND THE HUMAN PANGENOME REFERENCE CONSORTIUM. OTHER EFFORTS FOCUS ON DIVERSITY AND REPRESENTATION AMONG RESEARCHER

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award

region

US

value

1523558

unit

USD