Technology, machines, and AI significantly impact nearly every aspect of our daily lives. Yet, computing ethics knowledge remains largely inaccessible, especially to non-computer science students. To address this, we designed an inclusive, interdisciplinary course on computing ethics and pedagogical principles. As part of this course, students (N = 21) partnered with seven non-computer science faculty to co-create a computing ethics module for each of their courses. Building on the course's diverse content, rich class discussions, and engaging activities, students shaped the learning experiences of future students through the successful co-creation of curricular materials. Student reception of our course was overwhelmingly positive, though some students noted an imbalance in how responsibilities were shared with faculty. Through our novel approach to curriculum development, a broader range of students will have access to critical knowledge affecting their daily lives. By teaching students how to teach others, our approach expands who engages with computing ethics and helps democratize knowledge about technology and its social and ethical implications.

Research on gestures in human-robot interaction has largely focused on finding that children may learn better and enjoy interacting with robots that gesture more often. However, no research to date has examined how children themselves spontaneously gesture in the presence of a human vs. robot instructor. A child’s use of gesture might be indicative of engagement or rapport with the robot instructor and may provide key information about a robot instructor’s efficacy or opportunities for intervention. As such, the current study examines 5-8-year-old children’s rate of deictic and conventional gestures when being assisted by a robot vs. human instructor. Overall, we find age-related effects in children’s gestures relation to the specific instructors. There is a significant negative correlation between age and gesture rate when learning from the human instructor, but no significant correlation with the robot instructor. These results are discussed in relation to children’s perceptions of the instructor, task difficulty, and age-related cognitive development shifts.

Assistive robots will be more effective if they can accurately reason about the intentions and beliefs of the user (i.e., have Theory of Mind (ToM)). ToM benchmarks allow us to examine how well an artificial agent (e.g., robot) is able to do ToM reasoning in a given scenario. However, there is a need for ToM benchmarks that are more representative of the challenges faced in assistive robotics. Existing benchmarks from AI and HRI make simplifying assumptions, such as simply defined goals, plans that are indicative of goals, and no user errors. To address the challenges from relaxing these assumptions, we propose the Theory of Mind of Children Assembling Tangrams (ToMCAT) dataset. The data is derived from videos of children building tangram puzzles while being assisted by a social robot. As a baseline benchmark, we evaluated two approaches for how well they can recognize which puzzle this child is building based on a single observation. Analogical reasoning correctly recognized the puzzle more than 75% of the time and had perfect accuracy for puzzle states that were close to complete. However, an out-of-the-box commercial LLM correctly recognized the puzzle only 60% of the time and was accurate on less than 80% of the completed puzzles. Our results suggest that the ToMCAT dataset offers challenges for recognizing the intended puzzle of a child. Furthermore, the dataset provides opportunities to examine additional ToM reasoning capabilities. Overall, the ToMCAT dataset provides a useful benchmark to facilitate the advancement of ToM reasoning for assistive robotics.

There is a lack of access to critical knowledge on machine ethics and the impacts of technology on individuals and communities in everyday life. This project pioneers an inclusive curriculum design process to broaden accessibility to machine ethics education. Our approach uses a ''source'' course to develop materials for seven "target" courses. The source course is a machine ethics curriculum development course in which students and faculty collaboratively build curricular materials for integration into non-computer science courses. Here we describe the development of the ''source'' course using a curriculum co-creation process that leverages student and faculty expertise. The process emphasizes an inclusive design approach, rooted in continuous stakeholder feedback and consistent, transparent communication. The products of this process include course materials that incorporate underrepresented ethical frameworks. Additionally, it features peer-reviewed journal assignments that promote reflective learning and sharing of diverse perspectives, as well as a final module project in which students collaborate with faculty to co-create curricular materials. Our approach aims to broaden a culturally relevant understanding of ethical challenges in technology while ensuring that the curriculum resonates with diverse student backgrounds. Our presentation will describe key insights about the process and products of our curriculum design.

Assistive robots are becoming an increasingly important application platform for research in robotics, AI, and HRI, as there is a pressing need to develop systems that support the elderly and people with disabilities, with a clear path to market. Yet, what remains unclear is whether current autonomous systems are already up to the task or whether additional HRI work is needed to make these systems acceptable and useful. In this article, we report our efforts of developing and evaluating an architecture for a fully autonomous robot designed to assist older adults with Parkinson’s disease (PD) in sorting their medications. The main goal for the robot is to aid users in a manner that maintains the autonomy of the user by providing cognitive and social support with varying levels of assistance. We first evaluated the robot with subjects drawn from a pool of university students, which is common practice in experimental work in psychology and HRI. As the results were very positive, we followed up with an evaluation using people with Parkinson’s disease, who surprisingly had mostly negative outcomes. We thus report our analysis of the differences in the evaluations and discuss the challenges for HRI posed by the sources of the negative evaluations: (1) designing a robot to adapt to the many routines the participants use at home, (2) unique needs of participants with PD not present in student participants, and (3) the role of familiar technologies in designing and evaluating a new technology. While it is unlikely, given the current state of technology, that fully autonomous assistive robots for older adults will be available in the near term, we believe that our work exposes a critical need in HRI to involve the target population as early as possible in the design process.

A robot in the home of an elderly person providing assistive care will face many difficult decisions. I focus on a set of tasks that are very common and often stressful. Medication management tasks are ideal for a robot to assist in, but even a task with straightforward guidelines and goals can have numerous moral issues brought about by the social interaction between the human and the robot. Building on artificial intelligence work in production systems, decision theory, and analogical reasoning I am developing the architectural components necessary for these interactions. These components are based on computational models that have been informed by work in psychology and occupational therapy. It is my goal that by bringing these disciplines together I will be able to help design robots that are more morally acceptable, safer, and overall do a better job at assisting those in need.

While it has been well established that affect influences judgments and decision-making, few computational models of the phenomenon exist. The work I have done and propose focuses on the role of affect in complex decision-making or judgment tasks where a pure utilitarian approach does not reflect human behavior. It is especially challenging to develop models that can predict choices made by an individual. However, as we approach having companion robots that have long-term relationships with a user, it becomes increasingly vital for the robot to be sensitive to the affect of the human and to behave in a manner that is consistent with the expectations of the user and society. The models and underlying robot architecture I describe here bring us closer to robots being accepted in our homes.

Moral judgements are a complex phenomenon that have gained a renewed interest in the research community. Many have proposed explanations for moral judgements, including utilitarian accounts and the Principle of Double Effect. Some also advocate for the critical role of emotional processes like empathy. However, developing a computational model of moral judgements is rare perhaps due in part to the numerous influences on it. We present here a computational model of moral judgements based on moral expectation and the Principle of Double Effect. We then extend this model to provide a plausible explanation for the effect of empathy on these judgements. We evaluate these models using results from recent studies with human participants.