Addressing discretization-induced bias in demographic prediction.

ABSTRACT: Racial and other demographic imputation is necessary for many applications, especially in auditing disparities and outreach targeting in political campaigns. The canonical approach is to construct continuous predictions-e.g. based on name and geography-and then to often discretize the predictions by selecting the most likely class (argmax), potentially with a minimum threshold (thresholding). We study how this practice produces discretization bias. For example, we show that argmax labeling, as used by a prominent commercial voter file vendor to impute race/ethnicity, results in a substantial under-count of Black voters, e.g. by 28.2% points in North Carolina. This bias can have substantial implications in downstream tasks that use such labels. We then introduce a joint opt

SUBMITTER: Dong E 

PROVIDER: S-EPMC11803427 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature