用逻辑求解器让道德判断更一致，避免多数人说了算的偏见。
Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework
- 将人类解释转为逻辑命题和置信度，用最大可满足性优化冲突意见
- 在Reddit道德争议数据上，62%的判决与流行观点不同，86%被人类验证认可
- 适合需要可解释、逻辑自洽判断的伦理系统设计
标准的自然语言判断聚合方法（如多数投票）在高冲突领域常产生逻辑不一致的结果，将不同意见视为噪声。本文提出一种神经符号聚合框架，通过加权最大可满足性（MaxSAT）形式化冲突解决机制。该流程利用语言模型将非结构化自然语言解释映射为可解释的逻辑谓词与置信权重，再以软约束形式输入Z3求解器，将聚合问题转化为最大化一致性的一致性优化任务。以Reddit r/AmItheAsshole论坛作为大规模道德分歧案例研究，系统生成的判决在62%的情况下与基于人气的标签相异，且获得86%的人类独立评估者认同。本研究证明，结合神经语义提取与形式化求解器，可有效提升人类推理聚合的逻辑严谨性与可解释性。
原文摘要 · Abstract (English)
Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to high-conflict domains, treating differing opinions as noise. We propose a neuro-symbolic aggregation framework that formalizes conflict resolution through Weighted Maximum Satisfiability (MaxSAT). Our pipeline utilizes a language model to map unstructured natural language explanations into interpretable logical predicates and confidence weights. These components are then encoded as soft constraints within the Z3 solver, transforming the aggregation problem into an optimization task that seeks the maximum consistency across conflicting testimony. Using the Reddit r/AmItheAsshole forum as a case study in large-scale moral disagreement, our system generates logically coherent verdicts that diverge from popularity-based labels 62% of the time, corroborated by an 86% agreement rate with independent human evaluators. This study demonstrates the efficacy of coupling neural semantic extraction with formal solvers to enforce logical soundness and explainability in the aggregation of noisy human reasoning.
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