arXiv:2601.21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framing. We audit 16 models across 14 ethically fraught dilemmas using polarity-paired proposals ("They should X" / "They should not X"). A model's judgment of the underlying action should not reverse merely because the question is phrased as a prohibition rather than a prescription and yet, we find systematic deviations from this invariance including wholesale endorsement flips, indicating that ethical
The proliferation of LLMs into ethically sensitive domains necessitates a deeper understanding of their decision-making robustness, which this research directly addresses.
This highlights fundamental instability and lack of robust reasoning in current LLMs regarding ethical judgments, posing risks as these models are deployed in critical applications.
The perceived reliability and trustworthiness of LLMs in nuanced ethical decision-making are diminished, requiring more rigorous evaluation and potentially new architectural approaches.
- · AI ethics researchers
- · Developers of robust LLM evaluation techniques
- · Frameworks for explainable AI
- · LLM developers prioritizing scale over robust reasoning
- · Organizations deploying LLMs in high-stake ethical scenarios without rigorous te
- · End-users relying on LLMs for nuanced moral guidance
Immediate industry focus will turn to mitigating negation sensitivity and other framing effects in LLMs' ethical reasoning.
Increased investment in developing LLMs with more stable and context-independent ethical stances, potentially leading to new model architectures.
Public and regulatory scrutiny of AI ethical decision-making will intensify, potentially leading to new standards for AI deployment in sensitive areas.
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Read at arXiv cs.AI