Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making

TL;DR

This paper introduces and analyzes 17 socio-technical archetypes of roles assigned to LLMs in human-AI decision-making, highlighting how archetype selection impacts outcomes and system design considerations.

Contribution

It defines human-LLM archetypes, derives them from literature, evaluates their effects in clinical cases, and discusses tradeoffs for system design.

Findings

Archetype selection influences LLM outputs and decision quality.

Different archetypes entail tradeoffs in control and social hierarchy.

Design choices affect decision outcomes and risk management.

Abstract

LLMs are increasingly supporting decision-making across high-stakes domains, requiring critical reflection on the socio-technical factors that shape how humans and LLMs are assigned roles and interact during human-in-the-loop decision-making. This paper introduces the concept of human-LLM archetypes -- defined as re-curring socio-technical interaction patterns that structure the roles of humans and LLMs in collaborative decision-making. We describe 17 human-LLM archetypes derived from a scoping literature review and thematic analysis of 113 LLM-supported decision-making papers. Then, we evaluate these diverse archetypes across real-world clinical diagnostic cases to examine the potential effects of adopting distinct human-LLM archetypes on LLM outputs and decision outcomes. Finally, we present relevant tradeoffs and design choices across human-LLM archetypes, including decision control, social hierarchies, cognitive forcing strategies, and information requirements. Through our analysis, we show that selection of human-LLM interaction archetype can influence LLM outputs and decisions, bringing important risks and considerations for the designers of human-AI decision-making systems