Assessing Fairness in the Presence of Missing Data.

ABSTRACT: Missing data are prevalent and present daunting challenges in real data analysis. While there is a growing body of literature on fairness in analysis of fully observed data, there has been little theoretical work on investigating fairness in analysis of incomplete data. In practice, a popular analytical approach for dealing with missing data is to use only the set of complete cases, i.e., observations with all features fully observed to train a prediction algorithm. However, depending on the missing data mechanism, the distribution of complete cases and the distribution of the complete data may be substantially different. When the goal is to develop a fair algorithm in the complete data domain where there are no missing values, an algorithm that is fair in the complete case domain may show

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9043798 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature