Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making
This work addresses the socio-technical factors in human-AI collaboration for designers of decision-making systems, but it is incremental as it builds on existing interaction patterns.
The paper tackles the problem of understanding how roles are assigned to LLMs in human-AI decision-making by introducing 17 human-LLM archetypes derived from a review of 113 papers, and shows that selecting different archetypes can influence LLM outputs and decisions in clinical diagnostic cases.
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