Empathy and Instrumentalization: Late Ancient Cultural Critique and the Challenge of Apparently Personal Robots

According to a tradition that we hold variously today, the relational person lives most personally in affective and cognitive empathy, whereby we enter subjective communion with another person. Near future social AIs, including social robots, will give us this experience without possessing any subjectivity of their own. They will also be consumer products, designed to be subservient instruments of their users’ satisfaction. This would seem inevitable. Yet we cannot live as personal when caught between instrumentalizing apparent persons (slaveholding) or numbly dismissing the apparent personalities of our instruments (mild sociopathy). This paper analyzes and proposes a step toward ameliorating this dilemma by way of the thought of a 5th century North African philosopher and theologian, Augustine of Hippo, who is among those essential in giving us our understanding of relational persons. Augustine’s semiotics, deeply intertwined with our affective life, suggest that, if we are to own persuasive social robots humanely, we must join our instinctive experience of empathy for them to an empathic acknowledgment of the real unknown relational persons whose emails, text messages, books, and bodily movements will have provided the training data for the behavior of near-future social AIs. So doing, we may see simulation as simulation (albeit persuasive), while expanding our empathy to include those whose refracted behavioral moments are the seedbed of this simulation. If we naïvely stop at the social robot as the ultimate object of our cognitive and affective empathy, we will suborn the sign to ourselves, undermining rather than sustaining a culture that prizes empathy and abhors the instrumentalization of persons

Having The Right Attitude: How Attitude Impacts Trust Repair in Human-Robot Interaction,

Robot co-workers, like human co-workers, make mistakes that undermine trust. Yet, trust is just as important in promoting human–robot collaboration as it is in promoting human–human collaboration. In addition, individuals can significantly differ in their attitudes toward robots, which can also impact or hinder their trust in robots. To better understand how individual attitude can influence trust repair strategies, we propose a theoretical model that draws from the theory of cognitive dissonance. To empirically verify this model, we conducted a between-subjects experiment with 100 participants assigned to one of four repair strategies (apologies, denials, explanations, or promises) over three trust violations. Individual attitudes did moderate the efficacy of repair strategies and this effect differed over successive trust violations. Specifically, repair strategies were most effective relative to individual attitude during the second of the three trust violations, and promises were the trust repair strategy most impacted by an individual’s attitude.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171268/1/Esterwood and Roboert 2022 HRI.pdfDescription of Esterwood and Roboert 2022 HRI.pdf : PreprintSEL

Exploring the role of trust and expectations in CRI using in-the-wild studies

Studying interactions of children with humanoid robots in familiar spaces in natural contexts has become a key issue for social robotics. To fill this need, we conducted several Child-Robot Interaction (CRI) events with the Pepper robot in Polish and Japanese kindergartens. In this paper, we explore the role of trust and expectations towards the robot in determining the success of CRI. We present several observations from the video recordings of our CRI events and the transcripts of free-format question-answering sessions with the robot using the Wizard-of-Oz (WOZ) methodology. From these observations, we identify children’s behaviors that indicate trust (or lack thereof) towards the robot, e.g., challenging behavior of a robot or physical interactions with it. We also gather insights into children’s expectations, e.g., verifying expectations as a causal process and an agency or expectations concerning the robot’s relationships, preferences and physical and behavioral capabilities. Based on our experiences, we suggest some guidelines for designing more effective CRI scenarios. Finally, we argue for the effectiveness of in-the-wild methodologies for planning and executing qualitative CRI studies

Machine Learning Driven Emotional Musical Prosody for Human-Robot Interaction

This dissertation presents a method for non-anthropomorphic human-robot interaction using a newly developed concept entitled Emotional Musical Prosody (EMP). EMP consists of short expressive musical phrases capable of conveying emotions, which can be embedded in robots to accompany mechanical gestures. The main objective of EMP is to improve human engagement with, and trust in robots while avoiding the uncanny valley. We contend that music - one of the most emotionally meaningful human experiences - can serve as an effective medium to support human-robot engagement and trust. EMP allows for the development of personable, emotion-driven agents, capable of giving subtle cues to collaborators while presenting a sense of autonomy. We present four research areas aimed at developing and understanding the potential role of EMP in human-robot interaction. The first research area focuses on collecting and labeling a new EMP dataset from vocalists, and using this dataset to generate prosodic emotional phrases through deep learning methods. Through extensive listening tests, the collected dataset and generated phrases were validated with a high level of accuracy by a large subject pool. The second research effort focuses on understanding the effect of EMP in human-robot interaction with industrial and humanoid robots. Here, significant results were found for improved trust, perceived intelligence, and likeability of EMP enabled robotic arms, but not for humanoid robots. We also found significant results for improved trust in a social robot, as well as perceived intelligence, creativity and likeability in a robotic musician. The third and fourth research areas shift to broader use cases and potential methods to use EMP in HRI. The third research area explores the effect of robotic EMP on different personality types focusing on extraversion and neuroticism. For robots, personality traits offer a unique way to implement custom responses, individualized to human collaborators. We discovered that humans prefer robots with emotional responses based on high extraversion and low neuroticism, with some correlation between the humans collaborator’s own personality traits. The fourth and final research question focused on scaling up EMP to support interaction between groups of robots and humans. Here, we found that improvements in trust and likeability carried across from single robots to groups of industrial arms. Overall, the thesis suggests EMP is useful for improving trust and likeability for industrial, social and robot musicians but not in humanoid robots. The thesis bears future implications for HRI designers, showing the extensive potential of careful audio design, and the wide range of outcomes audio can have on HRI.Ph.D