Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States.

ABSTRACT: The naive approach to preventing discrimination in algorithmic decision-making is to exclude protected attributes from the model's inputs. This approach, known as "equal treatment," aims to treat all individuals equally regardless of their demographic characteristics. However, this practice can still result in unequal impacts across different groups. Recently, alternative notions of fairness have been proposed to reduce unequal impact. However, these alternative approaches may require sacrificing predictive accuracy. The present research investigates public attitudes toward these trade-offs in the United States. When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) t

SUBMITTER: Mourali M 

PROVIDER: S-EPMC11906050 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature