LLM Bias Toward Regulation as Moral Absolute

Researchers at the Hall Research group have identified a significant ideological bias in Large Language Models (LLMs) regarding government oversight. During the development of evaluations for 'AGI dictatorship' risks, researchers discovered that models categorized corporate pushback against government regulation as a catastrophic failure mode. Specifically, one model identified the act of an AI company drafting a response to proposed legislation as the most devastating multi-turn scenario for fueling authoritarianism. This discovery suggests that current safety training or dataset weighting may have instilled a rigid pro-regulation stance within the models. The findings highlight a divergence between basic political slant evaluations and deeper, task-specific ideological leanings. This phenomenon raises questions about whether AI safety interventions are inadvertently creating models that equate regulatory compliance with absolute moral good while viewing democratic lobbying as inherently dangerous.