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A Study on Fairness and Trust Perceptions in Automated Decision Making

Schöffer, Jakob ORCID iD icon; Machowski, Yvette; Kühl, Niklas ORCID iD icon

Automated decision systems are increasingly used for consequential decision making---for a variety of reasons. These systems often rely on sophisticated yet opaque models, which do not (or hardly) allow for understanding how or why a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield undesirable (e.g., unfair) outcomes because their sanity is difficult to assess and calibrate in the first place. In this work, we conduct a study to evaluate different attempts of explaining such systems with respect to their effect on people's perceptions of fairness and trustworthiness towards the underlying mechanisms. A pilot study revealed surprising qualitative insights as well as preliminary significant effects, which will have to be verified, extended and thoroughly discussed in the larger main study.

Verlagsausgabe §
DOI: 10.5445/IR/1000130551
Veröffentlicht am 18.08.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000130551
Erschienen in Joint Proceedings of the ACM IUI 2021 Workshops, April 13–17, 2021, College Station, USA
Veranstaltung 26th International Conference on Intelligent User Interfaces (IUI 2021), Online, 13.04.2021 – 17.04.2021
Verlag RWTH Aachen
Seiten 170005
Serie CEUR Workshop Proceedings ; 2903
Externe Relationen Konferenz
Schlagwörter Automated Decision Making, Fairness, Trust, Transparency, Explanation, Machine Learning
Nachgewiesen in arXiv
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