User experience (UX) research relies heavily on survey scales to measure users' subjective experiences with technology. However, repeatedly raised concerns regarding the improper use of in UX and adjacent fields call for a systematic review current measurement practices. Therefore, we conducted literature review, screening 153 papers from four years ACM Conference Human Factors Computing Systems proceedings (ACM CHI 2019 2022), which 60 were eligible empirical studies using study...

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 decisions. How to communicate members public that an has been audited considered remains open challenge. This study empirically investigated certification labels as promising solution. Through interviews (N = 12) census-representative survey 302), we end-users'...

Transparency is widely regarded as crucial for the responsible real-world deployment of artificial intelligence (AI) and considered an essential prerequisite to establishing trust in AI. There are several approaches enabling transparency, with one promising attempt being human-centered explanations. However, there little research into effectiveness explanations on end-users' trust. What complicates comparison existing empirical work that measured different ways. Some researchers measure...

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 main goals XAI. Nevertheless, empirical research on this topic inconclusive regarding effect trust. An explanation ambiguity could trust operationalized differently within In position paper, we advocate a clear distinction between behavioral (objective) measures reliance attitudinal (subjective)...

Trust is crucial for human interaction with artificial intelligence (AI) and frequently measured through questionnaires or rating scales. One commonly used questionnaire in AI research the between People Automation scale (TPA). However, its psychometric quality has yet to be examined context of AI. More recently, a Scale Explainable (TXAI) was recommended but not empirically evaluated. In this study, we assessed qualities both scales, using confirmatory exploratory factor analyses test...

Measuring the subjective experience of digital game players is essential to player research. Recently, Player Experience Inventory (PXI) was developed, which assesses both functional and psychosocial consequences gameplay. We present a pre-registered independent online study with large sample provide additional evidence psychometric quality for PXI. Responses from 1518 participants were collected, rating recent or memorable playing using PXI related measures. While our results standard...

Virtual reality (VR) applications are increasingly permeating our lives. The immersion provided by VR enables novel interactions with data that would be impossible in conventional environments. Especially regarding multimedia data, could overcome existing limitations when browsing videos to find specific scenes. This paper introduces a virtual drawer, tailored environments, enable ways interact videos. A within-subjects design experiment (N=24) was conducted evaluate the drawer on user...

Measuring the subjective experience of digital game players is essential to player research. Recently, Player Experience Inventory (PXI) was developed, which assesses both functional and psychosocial consequences gameplay. We present a pre-registered independent online study with large sample provide additional evidence psychometric quality for PXI. Responses from 1518 participants were collected, rating recent or memorable playing using PXI related measures. While our results standard...

Despite the importance of trust in human-AI interactions, researchers must adopt questionnaires from other disciplines that lack validation AI context. Motivated by need for reliable and valid measures, we investigated psychometric quality two questionnaires, Trust between People Automation scale (TPA) Jian et al. (2000) Scale Context (TAI) Hoffman (2023). In a pre-registered online experiment (N = 1485), participants observed interactions with trustworthy untrustworthy (autonomous vehicle...

User experience research relies heavily on survey scales to measure users' subjective experiences with technology. However, repeatedly raised concerns regarding the improper use of in UX and adjacent fields call for a systematic review current measurement practices. Therefore, we conducted literature review, screening 153 papers from four years ACM Conference Human Factors Computing Systems proceedings, which 60 were eligible empirical studies using study experiences. We identified 85...

Trust is a key motivation in developing explainable artificial intelligence (XAI). However, researchers attempting to measure trust AI face numerous challenges, such as different conceptualizations, simplified experimental tasks that may not induce uncertainty prerequisite for trust, and the lack of validated questionnaires context AI. While acknowledging these issues, we have identified further challenge currently seems underappreciated - potential distinction between one construct...

In explainable artificial intelligence (XAI) research, explainability is widely regarded as crucial for user trust in (AI). However, empirical investigations of this assumption are still lacking. There several proposals to how might be achieved and it an ongoing debate what ramifications explanations actually have on humans. our work-in-progress we explored two posthoc explanation approaches presented natural language a means AI. We examined the effects human-centered behavior financial...