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

arXiv: https://arxiv.org/abs/2605.00270 · HTML: https://mlsanity.com/paper/2605.00270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://mlsanity.com/paper/2305.09656/) (2023)
- [Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes](https://mlsanity.com/paper/2008.09094/) (2020)
- [Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models](https://mlsanity.com/paper/2501.17420/) (2025)
- [Fairify: Fairness Verification of Neural Networks](https://mlsanity.com/paper/2212.06140/) (2022)
- [Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework](https://mlsanity.com/paper/2506.14948/) (2025)
- [AITA Generating Moral Judgements of the Crowd with Reasoning](https://mlsanity.com/paper/2310.18336/) (2023)

## Abstract (arXiv)

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.