Work

Latent bias and the implementation of artificial intelligence in medicine

Year: 2020

Type: article

Abstract: Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. A seemingly fair model could involve, directly or indirectly, what we call "latent biases." Just as latent errors are generally described as errors "waiting to happen" in complex systems, latent biases are biases waiting to happen. Here we describe 3 major challenges related to bias in AI algorithms and propose several ways of managing them. There is an urgent need to address latent biases before the widespread implementation of AI algorithms in clinical practice.

Source: Journal of the American Medical Informatics Association

DOI: 10.1093/jamia/ocaa094

Authors: Matthew DeCamp, Charlotta Lindvall

Institutions: University of Colorado Anschutz, Brigham and Women's Hospital, Harvard University, Dana-Farber Cancer Institute

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Cambia Health Foundation

OpenAlex ID: W3037480398