To Trust or Distrust Trust Measures: Validating Questionnaires for Trust in AI

Fuente: arXiv
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Autori principali: Scharowski, Nicolas, Perrig, Sebastian A. C., Aeschbach, Lena Fanya, von Felten, Nick, Opwis, Klaus, Wintersberger, Philipp, Brühlmann, Florian
Natura: Preprint
Pubblicazione: 2024
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author Scharowski, Nicolas
Perrig, Sebastian A. C.
Aeschbach, Lena Fanya
von Felten, Nick
Opwis, Klaus
Wintersberger, Philipp
Brühlmann, Florian
author_facet Scharowski, Nicolas
Perrig, Sebastian A. C.
Aeschbach, Lena Fanya
von Felten, Nick
Opwis, Klaus
Wintersberger, Philipp
Brühlmann, Florian
contents Despite the importance of trust in human-AI interactions, researchers must adopt questionnaires from other disciplines that lack validation in the AI context. Motivated by the need for reliable and valid measures, we investigated the psychometric quality of two trust questionnaires, the Trust between People and Automation scale (TPA) by Jian et al. (2000) and the Trust Scale for the AI Context (TAI) by Hoffman et al. (2023). In a pre-registered online experiment (N = 1485), participants observed interactions with trustworthy and untrustworthy AI (autonomous vehicle and chatbot). Results support the psychometric quality of the TAI while revealing opportunities to improve the TPA, which we outline in our recommendations for using the two questionnaires. Furthermore, our findings provide additional empirical evidence of trust and distrust as two distinct constructs that may coexist independently. Building on our findings, we highlight the opportunities and added value of measuring both trust and distrust in human-AI research and advocate for further work on both constructs.
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id arxiv_https___arxiv_org_abs_2403_00582
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle To Trust or Distrust Trust Measures: Validating Questionnaires for Trust in AI
Scharowski, Nicolas
Perrig, Sebastian A. C.
Aeschbach, Lena Fanya
von Felten, Nick
Opwis, Klaus
Wintersberger, Philipp
Brühlmann, Florian
Human-Computer Interaction
Despite the importance of trust in human-AI interactions, researchers must adopt questionnaires from other disciplines that lack validation in the AI context. Motivated by the need for reliable and valid measures, we investigated the psychometric quality of two trust questionnaires, the Trust between People and Automation scale (TPA) by Jian et al. (2000) and the Trust Scale for the AI Context (TAI) by Hoffman et al. (2023). In a pre-registered online experiment (N = 1485), participants observed interactions with trustworthy and untrustworthy AI (autonomous vehicle and chatbot). Results support the psychometric quality of the TAI while revealing opportunities to improve the TPA, which we outline in our recommendations for using the two questionnaires. Furthermore, our findings provide additional empirical evidence of trust and distrust as two distinct constructs that may coexist independently. Building on our findings, we highlight the opportunities and added value of measuring both trust and distrust in human-AI research and advocate for further work on both constructs.
title To Trust or Distrust Trust Measures: Validating Questionnaires for Trust in AI
topic Human-Computer Interaction
url https://arxiv.org/abs/2403.00582