AI is reshaping workplace dynamics as people increasingly delegate tasks to intelligent assistants. Yet how AI delegates are perceived compared to human delegates—and how their performance and their received feedback shape perceptions—remains unclear. We conducted a 2×2×2 between-subject experiment where participants delegated a scheduling task to either a human or an AI agent, varying their competence (high vs. low) and valence of received feedback (positive vs. negative) toward their performance. Participants generally had higher trust in human assistants; yet a striking asymmetry emerged: when an AI assistant received negative feedback, participants felt the criticism as more self-directed—an “AI Phantom Limb” effect—whereas positive feedback transferred less. This asymmetry did not appear with human delegates. These findings highlight broader design implications, suggesting that AI delegation might blur the boundary between self and other. We also discuss how these findings extend theories of delegation and responsibility attribution to AI.

In this position paper, we argue that baseball-and sports more broadly-provide a unique and under-explored opportunity for researchers to study human-robot interaction (HRI) in real-world settings. Using the rise of robot umpires in baseball as a primary example, we examine emerging themes such as power dynamics among players and umpires, labor implications, and technical challenges. We emphasize the affordances and benefits of studying sports within HRI, including the integration of interdisciplinary perspectives, the large-scale deployment of robots, and the examination of their role in deeply rooted cultural practices.

Power is a fundamental determinant of social life, yet it remains elusive in Human-Robot Interaction (HRI). This paper unveils power's pervasive but largely unexplored role in HRI by systematically investigating its varied manifestations across HRI literature. We first introduce definitions of power and then delve into the existing HRI literature through a lens of power, examining studies that directly address power and those exploring power-related social configurations and concepts such as authority, dominance, and status. Leveraging Fiske and Berdahl's model and French and Raven's bases of power framework, we also explore the nuances of power in many HRI studies where power is not explicitly addressed. Finally, we propose power as a core concept to advance HRI--- explaining fragmented existing findings through a coherent theory and delineating a cohesive theoretical trajectory for future investigations.

Artificially intelligent (AI) agents such as robots are increasingly delegated power in work settings, yet it remains unclear how power functions in interactions with both humans and robots, especially when they directly compete for influence. Here we present an experiment where every participant was matched with one human and one robot to perform decision-making tasks. By manipulating who has power, we created three conditions: human as leader, robot as leader, and a no-power-difference control. The results showed that the participants were significantly more influenced by the leader, regardless of whether the leader was a human or a robot. However, they generally held a more positive attitude toward the human than the robot, although they considered whichever was in power as more competent. This study illustrates the importance of power for future Human-Robot Interaction (HRI) and Human-AI Interaction (HAI) research, as it addresses pressing concerns of society about AI-powered intelligent agents.

The increased use of algorithms to support decision making raises questions about whether people prefer algorithmic or human input when making decisions. Two streams of research on algorithm aversion and algorithm appreciation have yielded contradicting results. Our work attempts to reconcile these contradictory findings by focusing on the framings of humans and algorithms as a mechanism. In three decision making experiments, we created an algorithm appreciation result (Experiment 1) as well as an algorithm aversion result (Experiment 2) by manipulating only the description of the human agent and the algorithmic agent, and we demonstrated how different choices of framings can lead to inconsistent outcomes in previous studies (Experiment 3). We also showed that these results were mediated by the agent's perceived competence, i.e., expert power. The results provide insights into the divergence of the algorithm aversion and algorithm appreciation literature. We hope to shift the attention from these two contradicting phenomena to how we can better design the framing of algorithms. We also call the attention of the community to the theory of power sources, as it is a systemic framework that can open up new possibilities for designing algorithmic decision support systems.

Fast-paced contemporary life full of planned interaction usually makes people miss out on wonderful moments. We here present BubbleBot, a speculative robot designed to support serendipity of interactions in public space. After observations in public spaces and embodied design workshops, we have designed BubbleBot to be a peripheral public-space robot, bursting bubbles at passersby to invite serendipitous interactions. BubbleBot is a speculative robot to create magical moments among people with minimal peripheral social interaction. With this project, we aim at generating a conversation about the future roles and interaction paradigms of robots in public space.