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

Sheza Munir, Ahanaf Rodoshi, Sumin Lee, Feiran Chang, Xujie Si, Syed Ishtiaque Ahmed
Proceedings of the The 39th Canadian Conference on Artificial Intelligence, PMLR 318:636-649, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v318-munir26a, title = {Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework}, author = {Munir, Sheza and Rodoshi, Ahanaf and Lee, Sumin and Chang, Feiran and Si, Xujie and Ahmed, Syed Ishtiaque}, booktitle = {Proceedings of the The 39th Canadian Conference on Artificial Intelligence}, pages = {636--649}, year = {2026}, editor = {Bouzar-Benlabiod, Lydia and Leung, Carson}, volume = {318}, series = {Proceedings of Machine Learning Research}, month = {25--29 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v318/main/assets/munir26a/munir26a.pdf}, url = {https://proceedings.mlr.press/v318/munir26a.html}, 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.} }
Endnote
%0 Conference Paper %T Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework %A Sheza Munir %A Ahanaf Rodoshi %A Sumin Lee %A Feiran Chang %A Xujie Si %A Syed Ishtiaque Ahmed %B Proceedings of the The 39th Canadian Conference on Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Lydia Bouzar-Benlabiod %E Carson Leung %F pmlr-v318-munir26a %I PMLR %P 636--649 %U https://proceedings.mlr.press/v318/munir26a.html %V 318 %X 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.
APA
Munir, S., Rodoshi, A., Lee, S., Chang, F., Si, X. & Ahmed, S.I.. (2026). Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework. Proceedings of the The 39th Canadian Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 318:636-649 Available from https://proceedings.mlr.press/v318/munir26a.html.

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