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

TL;DR

This paper introduces a neuro-symbolic framework that uses logical reasoning and language models to aggregate conflicting human judgments, improving logical consistency and explainability.

Contribution

It presents a novel method combining neural semantic extraction with formal solvers for conflict resolution in moral judgments, outperforming traditional voting.

Findings

System achieves 86% agreement with human evaluators.

Diverges from popularity-based labels 62% of the time.

Demonstrates effective logical coherence in moral disagreement aggregation.

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.