Public attitudes on performance for algorithmic and human decision-makers.

ABSTRACT: This study explores public preferences for algorithmic and human decision-makers (DMs) in high-stakes contexts, how these preferences are shaped by performance metrics, and whether public evaluations of performance differ depending on the type of DM. Leveraging a conjoint experimental design, approximately 9,000 respondents chose between pairs of DM profiles in two high-stakes scenarios: pretrial release decisions and bank loan approvals. The profiles varied by type (human vs. algorithm) and three metrics-defendant crime rate/loan default rate, false positive rate (FPR) among white defendants/applicants, and FPR among minority defendants/applicants-as well as an implicit fairness metric defined by the absolute difference between the two FPRs. The results show that efficiency was the most i

SUBMITTER: Bansak K 

PROVIDER: S-EPMC11631221 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature