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Extended exposure to reliable automation may lead to overreliance as evidenced by poor responses to auto-mation errors. Individual differences in trust may also influence responses. We investigated how these factors affect response to automation errors in a driving simulator study comprised of stop-controlled and uncon-trolled intersections. Drivers experienced reliable vehicle automation during six drives where they indicated if they felt the automation was going too slow or too fast by pressing the accelerator or brake pedal. Engage-ment via pedal presses did not affect the automation but offered an objective measure of trust in automation. In the final drive, an error occurred where the vehicle failed to stop at a stop-controlled intersection. Drivers’ response to the error was inferred from brake presses. Mixture models showed bimodal response times and revealed that drivers with high trust were less likely to respond to automation errors than drivers with low trust. 

Abstract Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human–AI teaming perspectives on AI development similarly underscore. Co‐development strategies may also help reconcile efforts to develop performance‐based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences. 

Remote work presents a challenge to workers’ creativity, especially during the COVID-19 pandemic and the stay-at-home requirements. Individual differences in creativity, considered through the lens of distributional models, and their stability across different conditions are unknown. We assess the between-person variability in common metrics of creativity, despite sharing similar experiences of virtual reality and mindfulness. The paper also assesses the stability of an individual’s creativity over time. We measured the creativity of 20 remote-workers daily, during a 9-week study. Creativity was measured with respect to divergent thinking and convergent thinking. Distributional models show significant individual differences in variability of creativity. Stability analyses also revealed that individuals’ creativity is relatively unstable over time— both within and across conditions. Although one measure of divergent creativity was relatively stable, the other was not. We suggest more research should assess the extent of variability in creativity relative to individual differences and under different conditions. 

The concept of using automated vehicles as mobile workspaces is now emerging. Consequently, the in- vehicle environment of automated vehicles must be redesigned to support user interactions in performing work-related tasks. During the design phase, interaction designers often use personas to understand target user groups. Personas are representations of prototypical users and are constructed from user surveys and interview data. Although data-driven, large samples of user data are typically assessed qualitatively and may result in personas that are not representative of target user groups. To create representative personas, this paper demonstrates a data analytics approach to persona development for future mobile workspaces using data from the occupational information network (O*NET). O*NET consists of data on 968 occupations, each defined by 277 features. The data were reduced using dimensionality reduction and 7 personas were identified using cluster analysis. Finally, the important features of each persona were identified using logistic regression. 

One advantage of highly automated vehicles is drivers can use commute time for non-driving tasks, such as work-related tasks. The potential for an auto-mobile office—a space where drivers work in automated vehicles—is a complex yet underexplored idea. This paper begins to define a design space of the auto- mobile office in SAE Level 3 automated vehicles by integrating the affinity diagram (AD) with a computational representation of the abstraction hierarchy (AH). The AD uses a bottom-up approach where researchers starting with individual findings aggregate and abstract those into higher-level concepts. The AH uses a top-down approach where researchers start with first principles to identify means-ends links between system goals and concrete forms of the system. Using the programming language R, the means-ends links of AH can be explored statistically. This computational approach to the AH provides a systematic means to define the design space of the auto-mobile office.