The article argues that current evaluations of large language models’ moral competence focus mainly on whether outputs align with human moral values—the so‑called moral value problem—while neglecting the moral norm problem, which concerns the models’ ability to identify and apply context‑sensitive moral norms. It attributes this imbalance to the field’s reliance on descriptive ethics frameworks that emphasize value representation over normative application. The authors review existing benchmarks, highlight three gaps—lack of ground‑truth norm data, insufficient evaluation of intermediate reasoning, and limited focus on context‑relevant features—and propose a research agenda to develop formal normative representations, expert‑annotated datasets, and evaluation protocols that distinguish between value‑level and norm‑level competence.
arXiv:2510. 16380v2 Announce Type: replace-cross Abstract: As AI systems progress, we rely more on them to make decisions with us and for us.
arXiv:2603. 00048v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making.
arXiv:2606. 11635v1 Announce Type: cross Abstract: For highly capable AI systems to operate safely in dynamic, open-ended environments, they must be able to identify, understand, and respond to moral reasons for action, and constrain their behaviour accordingly.
arXiv:2607. 20461v1 Announce Type: cross Abstract: Present implementations of artificial intelligence (AI) ethics do not adequately take feelings, or affect, into account.
arXiv:2608. 12368v1 Announce Type: new Abstract: Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs).
The paper argues that AI alignment depends on a system’s ability to exhibit a coherent moral policy—stable, monotonic, decisive, and Pareto‑viable—rather than on any specific moral standard. The authors test nine large language models across varied moral scenarios and find that none maintain consistent verdicts, with surface‑form changes causing up to 99% shifts in outcomes. This indicates that current LLM agents lack the structural moral competence required for meaningful alignment.
arXiv:2607. 00002v1 Announce Type: new Abstract: Moral cognition has traditionally been modeled as adherence to fixed ethical theories--deontology, consequentialism, virtue ethics--implemented as static rules or value functions.
arXiv:2609.05437v1 Announce Type: new Abstract: Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g.,...
The paper investigates how large language models encode moral knowledge by training linear probes for each Moral Foundations Theory category and analyzing their geometric relationships. It finds that the model’s moral directions are largely independent yet share a common component, indicating integration rather than collapse into a single detector. This structure is consistent across architectures, emerges early in pre‑training, and reflects corpus statistics rather than the individualizing/binding distinction of Moral Foundations Theory.
The study investigates how large language models encode moral knowledge by training linear probes for each category of Moral Foundations Theory. It finds that the model’s representations for different moral foundations occupy distinct, largely independent dimensions yet share a common positive component, indicating an integrated but nuanced moral structure. This geometry is consistent across model architectures and scales, emerges early in pre‑training, and reflects corpus statistics rather than the individualizing/binding distinction of the theory.
arXiv:2605. 22660v2 Announce Type: replace-cross Abstract: Moral language is subtle and culturally variable, making it difficult to translate faithfully across languages.
arXiv:2606. 00013v1 Announce Type: cross Abstract: Social conformity is a well-documented phenomenon in which individuals shift their opinions towards those of a social majority.