Co-design of LLM-based preference agents: participation may drive overtrust
Large language models are increasingly used to simulate human preferences in research and practical applications, raising concerns about validation, misrepresentation, and exclusion. Co-designing agents with the people they represent is a promising way to address these concerns, but participation may also mask the problems it appears to solve. This paper explores that tension through a primarily qualitative study in which 12 participants co-designed personal preference agents in the domain of ho
Record details
Published: 23 July 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy
Retrieved: 27 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Co-design of LLM-based preference agents: participation may drive overtrust
arXiv cs.CY · 27 July 2026
Bespoke Visual Assistance: What and How do Blind and Low-Vision People Create with Agentic Programming?
arXiv cs.HC · 23 July 2026
From Grasping to Speaking: Generative AI-Based Environment-Grounded VR Communication Training for Autistic Individuals
arXiv cs.HC · 23 July 2026
StateAct: Program State, before Pixels, for Long-Horizon Computer-Use Agents
HuggingFace Daily Papers · 23 July 2026
Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills
HuggingFace Daily Papers · 23 July 2026
SceneActBench: Can Agents Act on the 3D Scenes They See?
HuggingFace Daily Papers · 23 July 2026
How to cite this record
ethics.ai (23 July 2026), “Co-design of LLM-based preference agents: participation may drive overtrust,” evidence record 13631, https://ethics.ai/record/13631 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.