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

2026-04-30

AI Summary

This work addresses the challenge of achieving logically consistent aggregation of moral judgments in high-conflict ethical dilemmas, where conventional methods like majority voting often misinterpret diverse viewpoints as noise. The authors propose a neurosymbolic integration framework that uniquely combines semantic extraction via large language models with formal constraint solving. Human moral judgments are mapped to logical predicates annotated with confidence scores, and a weighted MaxSAT optimization—implemented using the Z3 solver—enforces consistency across multiple ethical perspectives. Departing from popularity-based aggregation, this approach prioritizes logical coherence, yielding judgments that differ from majority voting in 62% of cases on the Reddit r/AmItheAsshole dataset while achieving 86% agreement with independent human evaluators, thereby significantly enhancing both interpretability and fairness.

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.

Problem

• ethical reasoning
• judgment aggregation
• logical consistency
• moral disagreement
• conflict resolution

Innovation

• neuro-symbolic reasoning
• Weighted MaxSAT
• ethical judgment aggregation
• logical consistency
• explainable AI