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doi:10.22028/D291-36739
Titel: | Trust in Artificial Intelligence: Comparing Trust Processes Between Human and Automated Trustees in Light of Unfair Bias |
VerfasserIn: | Langer, Markus König, Cornelius J. Back, Caroline Hemsing, Victoria |
Sprache: | Englisch |
Titel: | Journal of Business and Psychology |
Verlag/Plattform: | Springer Nature |
Erscheinungsjahr: | 2022 |
Freie Schlagwörter: | Artifcial intelligence Trust Personnel Selection AI ethics Errors |
DDC-Sachgruppe: | 150 Psychologie |
Dokumenttyp: | Journalartikel / Zeitschriftenartikel |
Abstract: | Automated systems based on artifcial intelligence (AI) increasingly support decisions with ethical implications where decision makers need to trust these systems. However, insights regarding trust in automated systems predominantly stem from contexts where the main driver of trust is that systems produce accurate outputs (e.g., alarm systems for monitoring tasks). It remains unclear whether what we know about trust in automated systems translates to application contexts where ethical considerations (e.g., fairness) are crucial in trust development. In personnel selection, as a sample context where ethical considerations are important, we investigate trust processes in light of a trust violation relating to unfair bias and a trust repair intervention. Specifcally, participants evaluated preselection outcomes (i.e., sets of preselected applicants) by either a human or an automated system across twelve selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds fve through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to potential unfair bias. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). The results of the online study showed that participants have initially less trust in automated systems. Furthermore, the trust violation and the trust repair intervention had weaker efects for the automated system. Those efects were partly stronger when highlighting system imperfection. We conclude that insights from classical areas of automation only partially translate to the many emerging application contexts of such systems where ethical considerations are central to trust processes. |
DOI der Erstveröffentlichung: | 10.1007/s10869-022-09829-9 |
URL der Erstveröffentlichung: | https://link.springer.com/article/10.1007/s10869-022-09829-9 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-367392 hdl:20.500.11880/33383 http://dx.doi.org/10.22028/D291-36739 |
ISSN: | 1573-353X 0889-3268 |
Datum des Eintrags: | 11-Jul-2022 |
Bezeichnung des in Beziehung stehenden Objekts: | Supplementary Information |
In Beziehung stehendes Objekt: | https://static-content.springer.com/esm/art%3A10.1007%2Fs10869-022-09829-9/MediaObjects/10869_2022_9829_MOESM1_ESM.docx |
Fakultät: | HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft |
Fachrichtung: | HW - Psychologie |
Professur: | HW - Prof. Dr. Cornelius König |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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Langer2022_Article_TrustInArtificialIntelligenceC.pdf | 1,18 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons