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.CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

In explainable artificial intelligence (XAI) research, explainability is widely regarded as crucial for user trust in artificial intelligence (AI). However, empirical investigations of this assumption are still lacking. There are several proposals as to how explainability might be achieved and it is an ongoing debate what ramifications explanations actually have on humans. In our work-in-progress we explored two posthoc explanation approaches presented in natural language as a means for explainable AI. We examined the effects of human-centered explanations on trust behavior in a financial decision-making experiment (N = 387), captured by weight of advice (WOA). Results showed that AI explanations lead to higher trust behavior if participants were advised to decrease an initial price estimate. However, explanations had no effect if the AI recommended to increase the initial price estimate. We argue that these differences in trust behavior may be caused by cognitive biases and heuristics that people retain in their decision-making processes involving AI. So far, XAI has primarily focused on biased data and prejudice due to incorrect assumptions in the machine learning process. The implications of potential biases and heuristics that humans exhibit when being presented an explanation by AI have received little attention in the current XAI debate. Both researchers and practitioners need to be aware of such human biases and heuristics in order to develop truly human-centered AI.

User experience research relies heavily on survey scales as an essential method for measuring users' subjective experiences with technology. However, repeatedly raised concerns regarding the improper use of survey scales in UX research and adjacent fields call for a systematic review of current measurement practice. Until now, no such systematic investigation on survey scale use in UX research exists. We, therefore, conducted a systematic literature review, screening 707 papers from CHI 2019 to 2021, of which 207 were eligible empirical studies using survey scales. Results show that papers frequently lacked rationales for scale selection (70.05%) and rarely provided all scale items used (21.74%). Nearly half of all scales were adapted (44.69%), while only one-fifth of papers reported any sort of scale quality investigation (19.32%). Furthermore, we identified 224 different scales and 287 distinct constructs measured. Most scales were used once (76.79%), and most constructs were measured once (81.88%). Results highlight questionable measurement practices in UX research and suggest opportunities to improve scale use for UX-related constructs. We provide recommendations to promote improved rigor in following best practices for scale-based UX research. This research was funded internally. All materials are available on OSF: https://osf.io/dgq6b/