In robotics, the concept of “dull, dirty, and dangerous” (DDD) work has been used to motivate where robots might be useful. In this paper, we conduct an empirical analysis of robotics publications between 1980 and 2024 that mention DDD, and find that only 2.7% of publications define DDD and 8.7% of publications provide concrete examples of tasks or jobs that are DDD. We then review the social science literature on “dull,” “dirty,” and “dangerous” work to provide definitions and guidance on how to conceptualize DDD for robotics. Finally, we propose a framework that helps the robotics community consider the job context for our technology, encouraging a more informed perspective on how robotics may impact human labor.

This research to practice full paper explores a new educational framework for AI-informed leadership and evaluates its curriculum and pedagogical approach through a novel, tailored, research instrument. Artificial Intelligence continues to rapidly transform many aspects of markets, solutions, and organizational culture across companies, agencies, and institutions in the public and private sectors. Within complex organizations, AI tools, technologies, and applications inform how leaders engage in strategy-making, management, operations, human resources, and professional education. Non-technical managers and executives are increasingly expected to lead teams to implement responsible AI solutions with the promise to improve efficiency, effectiveness, productivity, profitability, and more. AI is rapidly transforming organizational culture, requiring non-technical leaders to develop AI literacy and essential skills to lead teams in implementing responsible AI solutions. In the face of AI-driven change, business leaders need to be AI literate and develop their own essential skills, knowledge, procedures, and perspectives to successfully set vision and strategy to lead teams that can leverage AI to achieve inward-facing and outward-facing business goals. This presents challenges and opportunities to develop new pedagogical approaches and measures to prepare and assess business leaders' AI leadership skills - including understanding human-AI systems in the workplace and their responsible development and ethical use. There are also cultural and organizational behavior challenges in successfully adopting these new capabilities into a global and diverse human-AI workforce at scale. To advance these, we present an innovative hands-on AI leadership curriculum, where participants learn by making and team problem-solving, for United States Air Force (USAF) leaders to learn about AI and its responsible use in human-robot teaming with autonomous robots. We contribute new measures to assess their attitudinal shifts in AI leadership with respect to culture, mindsets, and ethics. We present a pilot study to evaluate our curriculum design and pedagogical approach to foster positive shifts in our AI leadership measures.

Due to an increased presence of robots in human-inhabited environments, we observe a growing body of examples in which humans show behavior that is indicative of strong social engagement towards robots that do not possess any life-like realism in appearance or behavior. In response, we focus on the under-explored concept of a common locus as a relevant driver for a robot passing a social threshold. The key principle of common locus is that sharing place and time with a robotic artifact functions as an important catalyst for a perception of shared experiences, which in turn leads to bonding. We present BlockBots, minimal cube-shaped robotic artifacts that are deployed in an unsupervised, open-ended and in-the-field experimental setting aimed to explore the relevance of this concept. Participants host the BlockBot in their domestic environment before passing it on, without necessarily knowing they are taking part in an experiment. Qualitative data suggest that participants make identity and mind attributions to the BlockBot. People that actively maintain a common locus with BlockBot by taking it with them when changing location, on trips and during outdoor activities, project more of these attributes than others.

Children are beginning to interact and develop rapport with social robots in their homes. These devices pose new concerns around marketing to children. These include questions of how advertisements can and should be embedded in a robot and the robot's persona and which methods of conveying advertisements to the user are deceptive. In this paper, we engage with 62 children ages 9–12 in an activity to design future robot advertising policies. Results demonstrate that children prefer robots to advertise to them through casual conversations, citing a more positive user experience and the benefit of personalized and conversationally relevant advertising. These findings illuminate a tension between child preferences and more deceptive advertising policies. Overall, the work presented in this paper prompts new design and legal policy questions for how and if robots should advertise to children.

We are delighted to guest edit this special law and policy issue of the Journal of Human-Robot Interaction. The issue comes at a time of heightened interest in robotics by policymakers at all levels. The HRI community is already deeply interdisciplinary and wide-ranging in its research questions. But to date, there has been relatively little work specifically focused on the ways robot design and user experience interacts with ongoing law and policy debates. This special issue is a chance to collect excellent examples of research at this intersection and, hopefully, to spark more.

People have been shown to project lifelike attributes onto robots and to display behavior indicative of empathy in human-robot interaction. Our work explores the role of empathy by examining how humans respond to a simple robotic object when asked to strike it. We measure the effects of lifelike movement and stories on people's hesitation to strike the robot, and we evaluate the relationship between hesitation and people's trait empathy. Our results show that people with a certain type of high trait empathy (empathic concern) hesitate to strike the robots. We also find that high empathic concern and hesitation are more strongly related for robots with stories. This suggests that high trait empathy increases people's hesitation to strike a robot, and that stories may positively influence their empathic responses.