Trust in Large Language Model chatbots depends not only on what these systems do but also on how their behavior is governed and communicated. We present Trust Mediator, a workbench that supports service owners in authoring and assessing principle sets for LLM-driven chatbots through persona-based exploration and structured scaffolds. To examine this workflow, we use three analytic lenses—specificity, coverage, and coherence—to characterize the principles produced. In an exploratory between-subjects study, we compared manual and assisted principle authoring. Participants in both conditions viewed principles as useful for governing and assessing chatbot behavior. Assisted authoring was generally perceived as more supportive and tended to broaden coverage. Manual authoring required more effort but yielded principles that were significantly more specific. These findings highlight complementary strengths of assisted and manual pathways, illustrating the value of treating principle sets as design objects within governance workflows. Beyond their analytic role in this study, the lenses also suggest opportunities for supporting the construction and inspection of principle sets.

Robots are increasingly present in spaces inhabited by humans. From a privacy-sensitive design perspective, they present challenges, as they acquire data about their environment to act autonomously and interact with their users. This may raise privacy concerns among robot users and bystanders. To address these concerns, we propose a multi-touchpoint design enabling users and bystanders to investigate how their privacy is protected. These touchpoints include (1) embodied interaction with the robot, either directly, whenever encountering a robot, or (2) later, in a dedicated physical space, (3) interaction with a virtual AI chatbot through a web site, and (4) interaction with a human Data Protection Officer. We evaluated this design and the usefulness of the proposed touchpoints in two studies. Our findings are threefold: first, all touchpoints are useful and complement each other; second, different people have different preferences; and third, the attributes of the situation (i.e. location, busyness, contextuality and sensitivity) impact the choice of the touchpoint they would use to ask their questions.

Various strategies have been explored for robots to prevent navigation blocks. However, such blocks may still happen and robots then need to resolve them. Blocks may happen either on the robot’s path or target location and may be caused either by human or object obstacles. In this paper we explore the design of robot behaviors to resolve such navigation blocks by asking humans for help. These behaviors combine different communication modalities in steps with increasing urgency. We focus on robots with limited sensing capabilities and present findings from an in-person experiment evaluating these behaviors. Our findings illustrate that humans can be more easily engaged to solve blocks caused by themselves rather than by third party objects. They also highlight the complexity of having robots with limited sensing capabilities successfully enact sequential interactions with their bystanders.

Robots powered by Artificial Intelligence require continuous sensing to function and interact autonomously with their environment. This may create concerns for their users. Here, we describe a study in the context of an office environment equipped with autonomous delivery robots. Our study aims to understand the employees’ current comprehension of the data captured by the robots, the nature of their concerns and ways to mitigate those. We found that the employees have little knowledge about data capture and that providing such knowledge can go either way, reassuring or generating new concerns which are often contextual. We qualitatively analysed reasons for having or not having concerns. Our findings include that trust in the employer is an important factor limiting them.