Relational Archetypes: A Comparative Analysis of AV-Human and Agent-Human Interactions

TL;DR

This paper proposes a taxonomy of relational archetypes to analyze human-AI agent interactions, inspired by autonomous vehicle traffic modulation studies, aiming to bridge HCI and AV research communities.

Contribution

It introduces a preliminary framework classifying human-AI interactions, fostering interdisciplinary dialogue and future research on societal impacts of AI agents.

Findings

Developed a taxonomy of relational archetypes based on literature review.

Extrapolated AV traffic modulation insights to human-AI interactions.

Encouraged scholarly debate on societal impacts of AI agents.

Abstract

Over the last couple of years, AI Agents have gained significant traction due to substantial progress in the capabilities of underlying General Purpose AI (GPAI) models, enhanced scaffolding techniques, and the promise to drive societal transformation. Companies, researchers, and policy makers have started to consider the different effects that AI agents may have across different dimensions of our lives. However, the literature exploring the broader effects of human-agent interactions is still underdeveloped. In this paper, we review the problem of traffic modulation by autonomous vehicles (AVs) in mixed traffic flows and extrapolate the learnings to the different modes of interaction between humans and AVs to the pair humans-AI agents. In doing so, we propose a preliminary taxonomy of relational archetypes based on literature on Human-Computer Interaction (HCI) and AV-human interaction and tentatively explore how the resulting framework may lead to new questions regarding human-agent interactions. Our effort is aimed at strengthening existing bridges between these two research communities, which share similar traits: autonomy, fast adoption, high impact, and great potential for economic transformation. Building on previous analogies between AI Agents and AVs (e.g., regarding autonomy levels), we anticipate this paper to spark scholarly debate on the different types of impact that agents may have on our societies, while inviting other researchers to expand the scope of their comparative analysis regarding AI Agents.