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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.

Auditing plays a pivotal role in the development of trustworthy AI. However, current research primarily focuses on creating auditable AI documentation, which is intended for regulators and experts rather than end-users affected by AI decisions. How to communicate to members of the public that an AI has been audited and considered trustworthy remains an open challenge. This study empirically investigated certification labels as a promising solution. Through interviews (N = 12) and a census-representative survey (N = 302), we investigated end-users' attitudes toward certification labels and their effectiveness in communicating trustworthiness in low- and high-stakes AI scenarios. Based on the survey results, we demonstrate that labels can significantly increase end-users' trust and willingness to use AI in both low- and high-stakes scenarios. However, end-users' preferences for certification labels and their effect on trust and willingness to use AI were more pronounced in high-stake scenarios. Qualitative content analysis of the interviews revealed opportunities and limitations of certification labels, as well as facilitators and inhibitors for the effective use of labels in the context of AI. For example, while certification labels can mitigate data-related concerns expressed by end-users (e.g., privacy and data protection), other concerns (e.g., model performance) are more challenging to address. Our study provides valuable insights and recommendations for designing and implementing certification labels as a promising constituent within the trustworthy AI ecosystem.

Imagine conducting a study to determine whether users rate the visual aesthetics of your website more positively than your competitors. To assess users' perceptions of both websites, you use a validated survey scale for visual aesthetics, and you observe a statistically significant difference in users' ratings of the visual aesthetics of the two websites of 0.5 on a 7-point Likert-type scale. However, determining whether such a difference is practically (and theoretically) meaningful is challenging. In this paper, I follow the procedure outlined in Anvari & Lakens (2021) to determine the smallest subjectively experienced difference in VisAWI-s ratings using an anchor-based method. A sample of N = 249 participants rated and compared screenshots of eight websites in an online survey. I determined an estimate of a population-specific mean difference of 0.4, or in POMP units 6.58%, which translates to a mean difference of 0.46 with the 7-point Likert-type scale of the VisAWI-s. These values suggest that differences in VisAWI-s scores exceeding these estimates, such as the 0.5 mentioned above, are likely noticeable and meaningful to users. However, the estimate of this smallest subjectively experienced difference is affected by the overall visual aesthetics rating of the stimuli used. Researchers can use this effect size to inform study design and sample size planning. Still, whenever possible, they should aim to determine a domain- and research-design-specific smallest effect size of interest., Comment: 11 pages, 10 figures

Trust is often cited as an essential criterion for the effective use and real-world deployment of AI. Researchers argue that AI should be more transparent to increase trust, making transparency one of the main goals of XAI. Nevertheless, empirical research on this topic is inconclusive regarding the effect of transparency on trust. An explanation for this ambiguity could be that trust is operationalized differently within XAI. In this position paper, we advocate for a clear distinction between behavioral (objective) measures of reliance and attitudinal (subjective) measures of trust. However, researchers sometimes appear to use behavioral measures when intending to capture trust, although attitudinal measures would be more appropriate. Based on past research, we emphasize that there are sound theoretical reasons to keep trust and reliance separate. Properly distinguishing these two concepts provides a more comprehensive understanding of how transparency affects trust and reliance, benefiting future XAI research., Comment: Will be published at the CHI 2022 Workshop on Trust and Reliance in AI-Human Teams. New Orleans '22: ACM Conference on Human Factors in Computing Systems