Equity in essence: a call for operationalising fairness in machine learning for healthcare

Abstract

Values are embedded throughout the MLHC pipeline, from the design of models, to the execution and reporting of trials, to the regulatory approval process. Guidelines hold significant power in defining what is worthy of emphasis. While fairness is essential to the impact and consequences of MLHC tools, the concept is often conspicuously absent or ineffectually vague in emerging guidelines. The field of machine MLHC has the opportunity at this juncture to render fairness integral to the identity field. We call on the MLHC community to commit to the project of operationalising fairness, and to emphasise fairness as a requirement in practice.

Ähnliche Arbeiten