We discuss the difference between understanding robot ethics as something that is grounded in philosophical ideas about a potential future design, and understanding robot ethics as something that is grounded in empirical data. We argue, that understanding "robots" as a relatively homogenous group of designs for which we can formulate general ethics may lead to a foresight of future robot designs that includes ideas and concerns that are not feasible or realistic. Our aim is to exemplify a complementing perspective, by shedding light on two different robotic designs. We discuss their relation to specific use practices and user experiences, and provide some early ethical reflections and design concerns.

The format used to present feedback from usability evaluations to developers affects whether problems are understood, accepted, and fixed. Yet, little research has investigated which formats are the most effective. We describe an explorative study where three developers assess 40 usability findings presented using five feedback formats. Our usability findings comprise 35 problems and 5 positive comments. Data suggest that feedback serves multiple purposes. Initially, feedback must convince developers about the relevance of a problem and convey an understanding of this. Feedback must next be easy to use and finally serve as a reminder of the problem. Prior to working with the feedback, developers rated redesign proposals, multimedia reports, and annotated screen dumps as more valuable than lists of problems, all of which were rated as more valuable than scenarios. After having spent some time working with the feedback to address the usability problems, there were no significant differences among the developers' ratings of the value of the different formats. This suggests that all of the formats may serve equally well as reminders in later stages of working with usability problems, but that redesign proposals, multimedia reports, and annotated screen dumps best address the initial feedback goals convincing developers that a usability problem exists and of conveying an understanding of the problem.

Usability evaluation is widely accepted as a valuable activity in software development. However, how results effectively are fed back to developers is still a relatively unexplored area. We argue that usability feedback can be understood as an argument for a series of usability problems, and that basic concepts from argumentation theory can help us understand how to create persuasive feedback. We revisit two field studies on usability feedback to study if concepts from Toulmin's model for argumentation and Aristotle's modes of persuasion can explain why some feedback formats outperform others. We recommend that evaluators specifically back up the warrants behind their usability claims, that their arguments use several modes of persuasion, and that they present feedback in browsable amounts not to overwhelm developers with information. For complex and controversial problems, we advise evaluators to involve developers in a learning process and provide the opportunity to experience and discuss the findings.

Think-aloud testing is a widely employed usability evaluation method, yet its use in practice is rarely studied. We report an explorative study of 14 think-aloud sessions, the audio recordings of which were examined in detail. The study shows that immediate analysis of observations made in the think-aloud sessions is done only sporadically, if at all. When testing, evaluators seem to seek confirmation of problems that they are already aware of. During testing, evaluators often ask users about their expectations and about hypothetical situations, rather than about experienced problems. In addition, evaluators learn much about the usability of the tested system but little about its utility. The study shows how practical realities rarely discussed in the literature on usability evaluation influence sessions. We discuss implications for usability researchers and professionals, including techniques for fast-paced analysis and tools for capturing observations during sessions.