Misguided Artificial Intelligence: How Racial Bias is Built Into Clinical Models
Atin Jindal
Abstract
Artificial Intelligence is being used today to solve a myriad of problems. While there is significant promise that AI can help us address many healthcare issues, there is also concern that health inequities can be exacerbated. This article looks specifically at predictive models in regards to racial bias. Each phase of the model building process including raw data collection and processing, data labelling, and implementation of the model can be subject to racial bias. This article aims to explore some of the ways in which this occurs.
Topics & Concepts
Computer scienceProcess (computing)Racial biasSubject (documents)Raw dataHealth careData scienceArtificial intelligenceRacismPolitical scienceWorld Wide WebProgramming languageLawOperating systemArtificial Intelligence in Healthcare and EducationHealthcare cost, quality, practicesEthics in Clinical Research