AI-Mediated Exchange Theory

TL;DR

This paper proposes AI-Mediated Exchange Theory (AI-MET), a framework extending Social Exchange Theory to better understand how AI influences human relationships and to unify diverse research perspectives.

Contribution

It introduces AI-MET as a novel theoretical framework to bridge divides among human-AI research communities and articulate AI's mediating role in social exchanges.

Findings

Initial ideas of mediation mechanisms are outlined.

AI-MET can facilitate interdisciplinary communication.

Framework aims to unify diverse research perspectives.

Abstract

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.