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cs.AI2026

Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning

Ali Dasdan, Manan Shah, W. Russell Neuman +3

Behavioral audits of Large Language Models on moral prompts measure what the model says, not the internal computation producing it. We use Transluce, an AI-driven mechanistic-inter…

cs.AI2026

Six Llamas: Comparative Religious Ethics Through LoRA-Adapted Language Models

Chad Coleman, W. Russell Neuman, Manan Shah +5

We present Six Llamas, a comparative study examining whether large language models fine-tuned on distinct religious corpora encode systematically different patterns of ethical reas…

cs.AI2025

The Convergent Ethics of AI? Analyzing Moral Foundation Priorities in Large Language Models with a Multi-Framework Approach

Chad Coleman, W. Russell Neuman, Ali Dasdan +2

As large language models (LLMs) are increasingly deployed in consequential decision-making contexts, systematically assessing their ethical reasoning capabilities becomes a critica…

cs.AI2025

Auditing the Ethical Logic of Generative AI Models

W. Russell Neuman, Chad Coleman, Ali Dasdan +2

As generative AI models become increasingly integrated into high-stakes domains, the need for robust methods to evaluate their ethical reasoning becomes increasingly important. Thi…

cs.AI2025

Analyzing the Ethical Logic of Eight Large Language Models

W. Russell Neuman, Chad Coleman, Manan Shah

This study examines the expressed ethical logic of eight prominent large language models from OpenAI, Meta, Perplexity, Anthropic, Google, Mistral, DeepSeek, and xAI. Each model an…