Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making

dc.contributor.author Schoeffer, Jakob
dc.contributor.author Machowski, Yvette
dc.contributor.author Kühl, Niklas
dc.date.accessioned 2021-12-24T17:26:11Z
dc.date.available 2021-12-24T17:26:11Z
dc.date.issued 2022-01-04
dc.description.abstract Automated decision systems (ADS) have become ubiquitous in many high-stakes domains. Those systems typically involve sophisticated yet opaque artificial intelligence (AI) techniques that seldom allow for full comprehension of their inner workings, particularly for affected individuals. As a result, ADS are prone to deficient oversight and calibration, which can lead to undesirable (e.g., unfair) outcomes. In this work, we conduct an online study with 200 participants to examine people’s perceptions of fairness and trustworthiness towards ADS in comparison to a scenario where a human instead of an ADS makes a high-stakes decision—and we provide thorough identical explanations regarding decisions in both cases. Surprisingly, we find that people perceive ADS as fairer than human decision-makers. Our analyses also suggest that people’s AI literacy affects their perceptions, indicating that people with higher AI literacy favor ADS more strongly over human decision-makers, whereas low-AI-literacy people exhibit no significant differences in their perceptions.
dc.format.extent 8 pages
dc.identifier.doi 10.24251/HICSS.2022.134
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79466
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Accountability, Evaluation, and Obscurity of AI Algorithms
dc.subject algorithmic decision-making
dc.subject explanations
dc.subject fairness
dc.subject perceptions
dc.subject study
dc.title Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making
dc.type.dcmi text
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