People underestimate the errors made by algorithms for credit scoring and recidivism prediction but accept even fewer errors.

ABSTRACT: This study provides the first representative analysis of error estimations and willingness to accept errors in a Western country (Germany) with regards to algorithmic decision-making systems (ADM). We examine people's expectations about the accuracy of algorithms that predict credit default, recidivism of an offender, suitability of a job applicant, and health behavior. Also, we ask whether expectations about algorithm errors vary between these domains and how they differ from expectations about errors made by human experts. In a nationwide representative study (N = 3086) we find that most respondents underestimated the actual errors made by algorithms and are willing to accept even fewer errors than estimated. Error estimates and error acceptance did not differ consistently for prediction

SUBMITTER: Rebitschek FG 

PROVIDER: S-EPMC8505498 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature