AI-Mediated Exchange Theory
This addresses the challenge of integrating findings across diverse fields like social computing and machine learning, though it is incremental as an extension of existing theory.
The paper tackles the problem of fragmented scholarly communities studying human-AI relationships by proposing AI-Mediated Exchange Theory (AI-MET), a framework that extends Social Exchange Theory to view AI as mediating human-to-human relationships, aiming to bridge divides and facilitate interdisciplinary communication.
As Artificial Intelligence (AI) plays an ever-expanding role in sociotechnical systems, it is important to articulate the relationships between humans and AI. However, the scholarly communities studying human-AI relationships -- including but not limited to social computing, machine learning, science and technology studies, and other social sciences -- are divided by the perspectives that define them. These perspectives vary both by their focus on humans or AI, and in the micro/macro lenses through which they approach subjects. These differences inhibit the integration of findings, and thus impede science and interdisciplinarity. In this position paper, we propose the development of a framework AI-Mediated Exchange Theory (AI-MET) to bridge these divides. As an extension to Social Exchange Theory (SET) in the social sciences, AI-MET views AI as influencing human-to-human relationships via a taxonomy of mediation mechanisms. We list initial ideas of these mechanisms, and show how AI-MET can be used to help human-AI research communities speak to one another.