A joint fairness model with applications to risk predictions for underrepresented populations.

ABSTRACT: In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: (i) fairness is often achieved by compromising accuracy for some groups; (ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a joint fairness mod

SUBMITTER: Do H 

PROVIDER: S-EPMC9363518 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature