Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/250060 
Year of Publication: 
2021
Citation: 
[Journal:] Scientific Reports [ISSN:] 2045-2322 [Volume:] 11 [Article No.:] 20171 [Publisher:] Springer [Place:] Berlin [Year:] 2021 [Pages:] 1-11
Publisher: 
Springer, Berlin
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 predictions made by algorithms or human experts, but people's living conditions (e.g. unemployment, household income) affected domain-specific acceptance (job suitability, credit defaulting) of misses and false alarms. We conclude that people have unwarranted expectations about the performance of ADM systems and evaluate errors in terms of potential personal consequences. Given the general public's low willingness to accept errors, we further conclude that acceptance of ADM appears to be conditional to strict accuracy requirements.
Subjects: 
Human behaviour
Information technolgy
Persistent Identifier of the first edition: 
Additional Information: 
Open access funding enabled and organized by Projekt DEAL.
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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