Dissertation Title: Robots as Social Catalysts: A Multidisciplinary Framework for Designing Embodied Social Agents that Foster Human Collaboration and Connection
As artificial intelligence (AI) devices become more common in our homes, concerns about their potential harm to human-human connections arise accordingly. This dissertation aspires to study the responsible design of embodied agents as social catalysts to purposefully enhance human-human interactions. It aims to shed light on the following three overarching research questions. Can we become more socially connected and collaborative with one another through the facilitation of a socially embodied agent? What social capabilities do these embodied agents need to acquire as social catalysts? What approaches should we take to design, develop and evaluate computing systems that enable positive social interactions between a human group and an embodied agent responsibly?
To investigate the three questions, this work proposes a multidisciplinary framework for the holistic design and evaluation of embodied social agents intended to foster human-human connection and collaboration. It argues that robots need to possess three social capabilities: social-affective perception, context awareness, and social adaptation. These capabilities are elaborated in detail within the framework, together with a comprehensive, iterative process for their design, evaluation, and enhancement. This process needs to be grounded in theories and findings in psychology, and employ a mixed-methods integrative approach that involve computing, social sciences, and interaction design.
A case study centered on parent-child reciprocal interaction is conducted to demonstrate and evaluate this proposed framework, highlighting the unique complexities and possibilities of multi-person human-robot interaction. The case study aims to facilitate enriching adult-child exchanges essential for children's development while overcoming various technological and methodological challenges posed by young children as a user group. A series of studies and experiments were conducted in this dissertation to examine all key aspects of the long-term multi-person human-agent interaction (M-HAI). These aspects include understanding the dynamics of human-human interaction, modeling social-affective dynamics in human-human interaction, introducing design guidelines for long-term M-HAI, and designing and evaluating adaptive M-HAI.
In summary, this dissertation provides insights into the potential of designing embodied social agents as social catalysts within human groups. It invites future exploration into the possibilities and challenges of machine-catalyzed group interactions, emphasizing both technical and ethical considerations. As sociable intelligent devices—from personal voice agents at home to autonomous vehicles—rapidly proliferate, humans increasingly interact with AI agents in an ecology composed of other humans and other intelligent machines. Accordingly, this work helps advance the social sophistication of intelligent machines that live with humans in this emergent human-agent ecology, as well as the understanding of the social and behavioral mechanisms underlying this ecology.
Dr. Cynthia Breazeal, Professor, MIT Media Lab
Dr. Rosalind Picard, Professor, MIT Media Lab
Dr. Malte Jung, Associate Professor, Cornell University
Dr. Hae Won Park, Research Scientist and Principal Investigator, MIT Media Lab