Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework

arXiv cs.AI / 5/4/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

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Key Points

The paper argues that common aggregation methods like majority voting can produce logically inconsistent outputs in high-conflict settings by treating disagreement as mere noise.
It proposes a neuro-symbolic aggregation framework that converts natural-language explanations into logical predicates with confidence weights, then encodes them as soft constraints for Weighted MaxSAT using Z3.
In a case study using Reddit’s r/AmItheAsshole, the system produces logically coherent verdicts and diverges from popularity-based labels 62% of the time.
The approach reportedly matches independent human evaluators with an 86% agreement rate, suggesting improved consistency and alignment over simple popularity proxies.
The study highlights the value of combining neural semantic extraction with formal optimization/solvers to improve logical soundness and explainability when aggregating noisy human judgments.

Abstract

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.