Co-design of LLM-based preference agents: participation may drive overtrust

2026-07-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

A qualitative study explores the tension in co-designing large language model (LLM)-based preference agents, specifically how participation might lead to overtrust. The research involved 12 participants who co-designed personal preference agents for household energy. While participants generally perceived their agents as representative, independent validation revealed mixed human-agent alignment. Agent responses were notably more homogeneous, decisive, and abstract compared to human samples. The author posits that process transparency and participation can function as an "overtrust engine," fostering trust while simultaneously concealing systematic misalignment. This mechanism is presented as a core concept in participatory preference agent design, framing individual alignment as an enacted process rather than a fixed state, with potential structural consequences at scale.

Key takeaway

For AI Ethicists and Research Scientists designing LLM-based preference agents, recognize that participatory co-design, while beneficial, can inadvertently foster overtrust. Your validation processes must extend beyond participant satisfaction to rigorously assess actual human-agent alignment. Systematically check for agent homogeneity, decisiveness, and abstraction, as these can mask underlying misrepresentations. Prioritize independent validation to mitigate the "overtrust engine" effect and prevent structural consequences at scale.

Key insights

Participation in co-designing LLM preference agents may foster overtrust, masking systematic misalignment between human and agent preferences.

Principles

• Participation can act as an "overtrust engine."
• Alignment is an enacted process, not a fixed state.
• Agent responses can be more homogeneous and decisive.

Method

A qualitative study involved 12 participants co-designing personal preference agents for household energy via a background survey, co-design interview, and validation survey.

In practice

• Independently validate co-designed agents.
• Assess agent homogeneity and decisiveness.

Topics

• LLM Preference Agents
• Participatory Design
• Overtrust
• Human-Agent Alignment
• Qualitative Study
• Energy Preferences

Best for: AI Scientist, AI Ethicist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.