Automated vehicles do not yet have clearly defined signaling methods towards other road users, which could complement natural communication practices with human drivers, such as eye contact or hand gestures. In order to establish trust, external human–machine interfaces (eHMIs) have been proposed, but so far, these have not been widely evaluated in natural traffic contexts. This paper presents a user study where 30 participants interacted with a functional display-based visual eHMI for an automated shuttle in mixed urban traffic. Two distinct features were investigated: the communication of (1) its awareness of different obstacles on the road ahead and (2) of its intention to start or to brake. The results indicate that the majority of participants in general regarded eHMIs as necessary for automated vehicles. When reflecting their experience with the eHMIs, about half of the participants experienced an increased comprehension and safety. The combined presentation of obstacle awareness and vehicle intentions helped more participants to understand the shuttle’s behavior than the presentation of obstacle awareness only, but fewer participants regarded this combination of awareness and intent to be safe. The strength of the found effects on subjective responses varied with regard to age and gender.

Demand-side management (DSM) programs aiming to both reduce and render household consumption more flexible are becoming increasingly essential due to ongoing energy crises and the growing integration of renewable energy into energy production. The active involvement of households and energy users is crucial to fully unlock the potential of DSM programs. As this paper demonstrates, despite more than thirty years of feminist scholarly work focusing on the home as an important site of the production of gender inequality, few of these insights have been taken into account by DSM designers. Additionally, we note a broader pattern concerning gaps in knowledge regarding the diverse perspectives of energy users and their domestic contexts, all of which create obstacles to successful rollout and scalability. This paper uses the concepts of the social license to automate and intersectionality to analyze the existing literature on DSM programs. We find that three primary barriers in household DSM programs have been addressed: 1) there is an unresolved tension between DSM technology being perceived as a masculine domain and the home as a feminine domain; 2) low-income households face challenges in accessing the technology needed to enable both flexibility and savings; and 3) disparities in opportunities for youth and the elderly to participate in DSM programs are insufficiently considered. Based on these findings we argue that user diversity—not only conceived of as separate identity category variables but also as implicating overlapping and possible mutually reinforcing marginalizations– is needed to form a starting point in DSM program design for fair and scalable solutions.

The energy transition requires increased end-user flexibility in consumption as a response to the more volatile production patterns of renewable resources. Automated demand-side management solutions can provide support in achieving this but struggle with trust and acceptance issues from end-users. This paper contributes insights into how communicating trustworthiness can be achieved through careful and context-aware interaction design in such systems. Core interface features such as feedback, automation transparency, control options, benefit information, and actionable information were identified and fifteen case studies from six countries were analysed with regard to provided interaction features, automation level, and end-user experiences. The results provide insights into the form and frequency of these features used at different automation levels and their varying role in trust and acceptance building. Based on our results, we recommend particular attention to providing actionable information and actively reaching out to users at a low automation level, to provision and communication of control at a medium automation level, and to providing transparency at a high automation level in order to promote trust successfully, while benefit information is central for a “social license to automate” independently of the automation level.

Despite the growing availability of data, simulation technologies, and predictive analytics, it is not yet clear whether and under which conditions users will trust Decision Support Systems (DSS). DSS are designed to support users in making more informed decisions in specialized tasks through more accurate predictions and recommendations. This mixed-methods user study contributes to the research on trust calibration by analyzing the potential effects of integrated reliability indication in DSS user interfaces for process management in first-time usage situations characterized by uncertainty. Ten experts specialized in digital tools for construction were asked to test and assess two versions of a DSS in a renovation project scenario. We found that while users stated that they need full access to all information to make their own decisions, reliability indication in DSS tends to make users more willing to make preliminary decisions, with users adapting their confidence and reliance to the indicated reliability. Reliability indication in DSS also increases subjective usefulness and system reliability. Based on these findings, it is recommended that for the design of reliability indication practitioners consider displaying a combination of reliability information at several granularity levels in DSS user interfaces, including visualizations, such as a traffic light system, and to also provide explanations for the reliability information. Further research directions towards achieving trustworthy decision support in complex environments are proposed.

Automation and AI increasingly reshape work systems beyond task execution, influencing how human capabilities are used, preserved, or transformed. Cognitive ergonomics traditionally evaluates automation through constructs such as workload, situation awareness, trust, and function allocation. While these perspectives explain important aspects of performance and interaction quality, they do not provide an integrated lens for evaluating how automation shapes human capabilities over time. This paper introduces the Human Potential in Automation (HPA) framework, a capability-centred evaluative framework that integrates perspectives from cognitive ergonomics, work design, and human-centred AI through four dimensions: Optimisation, Retention, Expansion, and Engagement. To operationalise the framework, we introduce a lightweight tool (“action cards”) to support participatory design reflection, and propose a concise 12-item companion scale to enable systematic evaluation of capability outcomes. We illustrate the framework through an exploratory multi-case study involving four industrial stakeholders in AI-supported quality control. The findings demonstrate the practical utility of the framework for identifying capability-related design considerations that extend beyond traditional performance-focused evaluation, contributing an integrative capability-centred perspective for the design and evaluation of automated work systems.