Eli McGraw

This panel, consisting of John Sullins, Robin Zebrowski, Antonio Chella, Eli McGraw, and Shannon Vallor will engage in questions around the role of sociomorphing and artificial phronesis in interpreting human-robot interactions, the ways that access to a robot’s inner speech allows for more ethical interactions and more accurate understanding of those interactions, and the role that these considerations have in XAI, ethical AI, and embodied social cognition in particular. We will also use this case study to discuss the OASIS framework offered by Seibt et al. in which sociomorphing is embedded, in order to see how the ethical stance of artificial phronesis invites more nuance into the social interaction, moving beyond mere simulation into enaction.

AI (broadly speaking) as a discipline and practice has tended to misconstrue social cognition by failing to properly appreciate the role and structure of the interaction itself. Participatory Sense-Making (PSM) offers a new level of description in understanding the potential role of (particularly robotics-based) AGI in a social interaction process. Where it falls short in distinguishing genuine living sense-makers from potentially cognitive artificial systems, sociomorphing allows for gradations in how these potential systems are defined and incorporated into asymmetrical sociality. By side-stepping problems of anthropomorphism and muddy language around it, sociomorphing offers a framework and ontology that can help researchers make finer distinctions while studying social cognition through enactive sociality, PSM. We show here how PSM and sociomorphing, taken together and reconceived for more than just social robotics, can offer a robust framework for AGI robotics-based approaches.

Within artificial intelligence (AI) and machine consciousness research, social cognition as a whole is often ignored. When it is addressed, it is often thought of as one application of more traditional forms of cognition. However, while theoretical approaches to AI have been fairly stagnant in recent years, social cognition research has progressed in productive new ways, specifically through enactive approaches. Using participatory sense-making (PSM) as an approach, we rethink conceptions of autonomy and openness in AI and enactivism, shifting the focus away from living systems to allow incorporation of artificial systems into social forms of sense-making. PSM provides an entire level of analysis through an overlooked autonomous system produced via social interaction that can be both measured and modeled in order to instantiate and examine more robust artificial cognitive systems.

In the following work, we introduce the mathematical and theoretical underpinnings of dynamical systems theory and enactivism and extend them to child-robot interaction. We believe this approach leads to more tangible methods for studying such interactions. Dynamical Systems Theory (DST) are described and applied through Participatory Sense-Making (PSM), an enactive approach to social cognition. While PSM does well to lay out a new level of analysis for social interactions between humans, robots don’t cleanly fit in. We propose here that a child’s perception of a robot as a genuine sense-maker allows the interaction process to be considered a dynamic and coupled system. Perceived sociality is integral to creating and sustaining a meaningfully dynamic interaction, and we call for finer distinctions to that end while studying child-robot interaction. Our proposed spectrum of perceived sociality, informed and grounded by a dynamical systems approach to child development and theories aimed at socially categorizing a robot interactor, such as sociomorphing, operationalizes the study of child-robot interactions. This approach enhances dynamical and enactive systems methodologies in developmental research and human-robot interaction studies.

In this paper, we use the recent appearance of LLMs and GPT-equipped robotics to raise questions about the nature of semantic meaning and how this relates to issues concerning artificially-conscious machines. To do so, we explore how a phenomenology constructed out of the association of qualia (defined as somatically-experienced sense data) and situated within a 4e enactivist program gives rise to intentional behavior. We argue that a robot without such a phenomenology is semantically empty and, thus, cannot be conscious in any way resembling human consciousness. Finally, we use this platform to address and supplement widely-discussed concerns regarding the dangers of attempting to produce artificially-conscious machines.