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AI-Mediated Proposals Show Instability and Hidden Alternatives in Collective Decision-Making
A study of four LLM mediators found that proposals generated from citizen opinions are unstable: re-running with different seeds or order changes assignments in 17-20% of cases, and reordering opinions affects assignments nearly as much as replacing them. Human annotators confirmed real differences, though an LLM judge overestimated frequency. Variation persists even with safeguards like temperature zero, and prompts seeking agreement underrepresent minority views. The authors propose audits to expose alternative assignments and disclose the objective behind each proposal.
Why it matters: This research reveals a concrete, practical risk in AI-mediated collective decision-making, with actionable audit recommendations for transparency.
AI-mediated deliberationLLM reliabilitytransparency