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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

Abstract:

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.

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
Scopus

Verlagsausgabe §
DOI: 10.5445/IR/1000130551
Veröffentlicht am 18.08.2021
Scopus
Zitationen: 2
Seitenaufrufe: 270
seit 16.03.2021
Downloads: 95
seit 19.08.2021
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