mlsanityData to 14 Oct 2026

Appearing Legitimate is Not Enough: Interrogating Synthetic Agents in Representational Processes through a Participatory Design Lens

arXiv 2608.17099 ↗First posted 17 Aug 2026cs.HC cs.CYcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Synthetic agents built atop LLM-based foundation models are gaining popularity as substitutes for human participants across research contexts, including user-testing, market-research, computational social science, surveys, and qualitative research. We are also witnessing an extension of synthetic agents into experimental implementations of policy consultation, jury deliberation, humanitarian diplomacy, and similar contexts where human participation and representation are central to the perceived legitimacy of the institutional processes. The value of participation extends beyond informational contributions and consensus generation; participation is a necessary, legitimizing condition for democratic political institutions and processes. Treating synthetic agents as human substitutes raises serious political, representational, and ethical concerns. Participatory Design's modes of engagement --- probing, priming, understanding, and generating --- offer helpful tools for engaging with representational questions of personhood. We apply the lens to three case studies of synthetic agents substituting for personhood at varying representational scales: local policy, enterprise jury deliberation, and global diplomacy. We argue that legitimacy and personhood are integral and mutually constitutive while identifying the ethical, representational, and methodological risks of using synthetic agents in representational processes. We conclude by proposing soft and hard boundaries for designing oversight on LLMs and synthetic agents in representational processes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 8 of its 69 references that are in the map. First posted on arXiv 17 Aug 2026.

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  1. 202612 Angry AI Agents: Evaluating Multi-Agent LLM Decision-Making Through Cinematic Jury Deliberation3 May

Other references · 7

  1. 2024Large language models that replace human participants can harmfully misportray and flatten identity groupscited by 144
  2. 2021Can Machines Learn Morality? The Delphi Experimentcited by 141
  3. 2024Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Modelscited by 105
  4. 2025Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Frameworkcited by 16
  5. 2025A Pragmatic View of AI Personhoodcited by 12
  6. 2023How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulationcited by 11
  7. 2025Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Predictioncited by 5

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