Mitigating the impact of biased artificial intelligence in emergency decision-making

Hammaad Adam; Aparna Balagopalan; Emily Alsentzer; Fotini Christia; Marzyeh Ghassemi

doi:10.1038/s43856-022-00214-4

Communications Medicine (Nov 2022)

Mitigating the impact of biased artificial intelligence in emergency decision-making

Hammaad Adam,
Aparna Balagopalan,
Emily Alsentzer,
Fotini Christia,
Marzyeh Ghassemi

Affiliations

Hammaad Adam: Institute for Data Systems and Society, Massachusetts Institute of Technology
Aparna Balagopalan: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Emily Alsentzer: Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology
Fotini Christia: Institute for Data Systems and Society, Massachusetts Institute of Technology
Marzyeh Ghassemi: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

DOI: https://doi.org/10.1038/s43856-022-00214-4
Journal volume & issue: Vol. 2, no. 1
pp. 1 – 6

Abstract

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Adam et al. evaluate the impact of biased AI recommendations on emergency decisions made by respondents to mental health crises. They find that descriptive rather than prescriptive recommendations made by the AI decision support system are more likely to lead to unbiased decision-making.

Published in Communications Medicine

ISSN: 2730-664X (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Medicine
Website: https://www.nature.com/commsmed/

About the journal