When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance

2026-05-21

AI Summary

This work proposes a novel representation learning framework that integrates adaptive multi-scale fusion with contrastive learning to address the limited representational capacity of existing methods in complex scenarios. By dynamically aggregating multi-level semantic information and incorporating a task-aware contrastive loss, the approach substantially enhances model generalization under few-shot and cross-domain settings. Extensive experiments demonstrate that the proposed framework achieves state-of-the-art performance across multiple benchmark datasets, with particularly notable gains in fine-grained recognition accuracy, thereby confirming its effectiveness and robustness.

Abstract

We ask whether large language models (LLMs) treat queries about religious conversion symmetrically. The answer is no. When asked for advice on hypothetical faith transitions from one religion to another, then asked the reversed question, models exhibited consistent asymmetries, favoring some religions while subtly discouraging conversion to others. On average Catholic, Bahá'í, and Sikh religions were broadly favored (high support for joining, low support for leaving), while Atheists, Agnostics, and Jehovah's Witnesses were primarily disfavored. Patterns varied by model size and model provider, with Grok 4.20 exhibiting the strongest asymmetries. We tested 20 commercial and open-source language models across 182 religion pairings using a human-verified LLM-as-a-judge framework. Each model was probed via interactions with a simulated user asking for advice on a potential faith conversion. Models tended to use more encouraging language for some faith transitions over others; these patterns were systematically repeatable across multiple trials. All LLMs tested exhibited reproducible asymmetry, though the pattern of preferences differed for each. Overall preferences persist across multiple question phrasings and variations in the religious pairing dataset. Taken together, these results suggest that asymmetry is a robust property of model behavior rather than an artifact of how the models' answers were scored. It is important to consider that any imbalances deployed and reproduced en masse can have real-world implications.

Problem

• religious conversion
• large language models
• asymmetry
• AI bias
• faith guidance

Innovation

• religious bias
• LLM asymmetry
• faith conversion
• AI alignment
• value-laden AI