Artificial intelligences or AI are increasingly being embodied and embedded in the world to carry
out tasks and support decision-making with and for people. Regardless of the mode of embodiment,
AI are increasingly being found within social contexts. Yet, not all human-AI interaction or
HAI contexts lead to the embodied AI being socially embodied. The social embodiment of
AI is a dynamic phenomenon that emerges from a certain combination of factors
that we can recognize but as yet do not have a way of meaningfully describing or defining. As such,
we argue that a new category is needed, one that can be applied to a variety of systems, contexts,
use cases, and user experiences in a dynamic way. We propose to call this category socially
embodied AI and the dynamic threshold demarcating merely embodied AI from socially embodied AI the
Tepper Line. Central is the notion of embodiment. Importantly, the AI has direct contact with the
world and is not merely a hidden process that facilitates social interactions among people.
Yet, interacting with people is not sufficient for social interaction.
An AI designed to be socially interactive may also be insufficient. Social processing is
bi-directional and human models may not apply to machines. Even so, decades of work has shown that
people unknowingly tend to apply human models to computer systems that have anthropomorphic cues,
even simple ones. Similarly, work in social robotics has shown that a link can be established
between embodiment and social embeddedness or situatedness. Recent work on machines and agents
has pinpointed the AI as a common denominator among embodiment types, as well as highlighted
the potential of such AI-based agents, interfaces, environments, and systems as participants, rather
than simple mediators. But what is artificial intelligence? Nilsson suggested that people
must perceive the existence of intelligence in an artificial agent for it to be seen as intelligent.
Social intelligence relates to skill in social interactions. It too is difficult to define, but
relevant models also highlight human perception as a key ingredient. People are biased to see
social intelligence even when there is none. For AI, it may be constructed through HAI experiences
based on the perceptions of the person or people involved rather than being a product
of the engineering of the creators. We propose the term socially embodied AI, the dynamic state that
an embodied AI attains when it is perceived by a person as a social agent during interaction.
It can apply regardless of form factor, morphology, or "body," actual intelligence,
intentions of the creators, and perceptions of others. As a state, it may be lost or regained
depending on the stability of the factors leading the person to perceive social embodiment. Hence,
it is a circumstantial category. Our review of the literature provided 71 descriptors
and concepts for defining AI in various ways. To validate the term and concept externally,
we conducted an open card sort with experts. The general embodied AI ontology is presented here.
The Person in the Embodied AI are subclasses of the Participant class. Both participate
in an Interaction. The Interaction is part of a Situation, which has a Context and a Purpose. In
the socially embodied AI ontology, the Person has a Perception of the Interaction that is Social.
The social perception is encouraged and enforced by the Re/action, Morphology,
and Intelligence properties of the Embodied AI. The red line is the Tepper Line, which can appear
or disappear over the course of one or more HAI experiences. To illustrate the Tepper Line,
we discussed two case studies: SideBot, a social robot, and Apple's Siri, a voice assistant.
After being placed in an older adult's home for a week, SideBot moved from an awkward
yet curious intrusion to something of a true companion. Siri, in contrast, moved dramatically
between mere machine and something of an older inattentive sibling when used as a real-time
second language support tool. In conclusion, we have captured the dynamic sociality of embodied
AI through a literature-informed ontology that was validated in a card sort exercise with experts.
In two case studies, we have highlighted a threshold—the Tepper Line—to demarcate
embodied and socially embodied AI. Future work will need to put the ontology to the test. We
also only involved experts in robotics and AI for our card sort. Future work can further validate
and test the socially embodied AI concept with non-experts or a mix of lay people and experts.