mlsanityData to 7 Oct 2026

Rules Before Oracles: Auditable, User-Configurable Argument Selection for Deliberative Polling

arXiv 2608.23979 ↗First posted 25 Aug 2026cs.AI cs.CR cs.CYcited by 0

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Abstract (arXiv)

In a deliberative poll, once submissions outnumber what anyone will read, some mechanism chooses which arguments each voter sees, acquiring much of the decision; practice delegates it to opaque learned rankers, so a voter cannot recompute or contest the exposure that shaped their vote. We ask whether it can be a published rule over publicly recomputable evidence with parameters held by the voter, treating legibility as an admissibility condition on usable mechanisms, not an objective traded against accuracy. We formalise a poll over bipolar justification sets, judging a slate by reason coverage, the order it arrives in, and captured endorsement mass; we give seven checkable criteria for a civic recommender and a rule meeting them: a one-hop reversed endorsement flow parameterised by a relation-weight function. An agentic simulator records every slate at every vote, over about 17,000 seed-paired runs. Served slates fall 0.035 short of a label-reading ceiling upper-bounding every selection procedure, opaque ones included: any unconstrained ranker's advantage is bounded and small. On coverage alone, with non-degenerate authoring, the rule is indistinguishable from a random slate, a null due to an order-blind, charity-blind instrument; on the other two it leads at every prefix by a margin widening with adversarial pressure and dominates on mass by a factor of 3.3. Once a realistic fraction of submissions carries no reasons, the coverage margin returns and grows. Label-homogeneous flooding collapses completeness from 0.81 to 0.34 under a flat weight policy, only to 0.44 under author-count normalisation, making the weight function a security control worth 10% of completeness. The choice between ranking arms is a position on a coverage-versus-mass frontier, not a fact, the kind of choice only a legible rule can hand to the person it affects. It maps onto an open-source peer-to-peer platform.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 98 references that are in the map. First posted on arXiv 25 Aug 2026.

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12 of 98 references.

References · 12

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  2. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  3. 2016The Mythos of Model Interpretabilitycited by 716
  4. 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
  5. 2017Multisided Fairness for Recommendationcited by 69
  6. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  7. 2022Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approachescited by 23
  8. 2023Proportional Aggregation of Preferences for Sequential Decision Makingcited by 17
  9. 2024Value Preferences Estimation and Disambiguation in Hybrid Participatory Systemscited by 7
  10. 2019Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Additioncited by 2
  11. 2014Recommending the Most Encompassing Opposing and Endorsing Arguments in Debatescited by 1
  12. 2020Representative Committees of Peerscited by 1

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  7. 2026Minority Sentinel: When to Overturn Majority Voting in Multi-Agent LLM Debates
  8. 2025Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents