Follow the Norm: Accounting for Fine-Tuning and Prompt Effects on Model Rationales

Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge. We propose treating the AI system as a proxy actor and test whether dataset-level norms can shift it away from its baseline safety behavior when it faces high-conflict dilemmas. We make three contributions. First, we demonstrate in controlled experiments that norm-breaking fine-tuning yields norm-divergent actions justified by se

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ethics.ai (13 August 2026), “Follow the Norm: Accounting for Fine-Tuning and Prompt Effects on Model Rationales,” evidence record 19176, https://ethics.ai/record/19176 (originally published by arXiv).

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