Considering Biased Data as Informative Artifacts in AI-Assisted Health Care

ABSTRACT Artificial intelligence (AI) tools used in medicine, like AI used in other fields, work by detecting patterns in large volumes of data. AI tools are able to detect these patterns because they can “learn,” or be trained to recognize, certain features in the data. However, medical AI tools trained with data that are skewed in some way can exhibit bias, and when that bias matches patterns of injustice, the use of the tools can lead to inequity and discrimination. Technical solutions such as attempting to fix biased clinical data used for AI training are well intentioned, but what undergirds all these initiatives is the notion that skewed clinical data are “garbage,” as in the computer science adage “garbage in, garbage out.” Instead, we propose thinking of clinical data as artifacts that, when examined, can be informative of societies and institutions in which they are found.

Contributors: Kadija Ferryman, Maxine Mackintosh