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.
arXiv:2608. 14522v1 Announce Type: new Abstract: As AI systems make more morally loaded decisions across society, one response has been moral preference elicitation.
arXiv:2604. 24155v3 Announce Type: replace-cross Abstract: The project of aligning machine behavior with human values raises a basic problem: whose moral expectations should guide AI decision-making?
The paper reports the first empirical study comparing how humans and large language models (LLMs) evaluate perceived moral agency (PMA) in both human and autonomous artificial agents within smart city scenarios. Using a validated PMA scale, 190 human participants and various LLMs were assessed, revealing that humans are perceived to have higher moral agency than artificial agents. When confronted with moral dilemmas, LLMs focus on situational factors such as harm severity and urgency, mirroring the context‑sensitivity observed in human raters.
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.
How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of...
arXiv:2608.24748v1 Announce Type: cross Abstract: How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixe...
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. 05680v1 Announce Type: cross Abstract: AI systems are increasingly used to provide legal advice, raising questions about whether laypeople accept guidance from algorithms--especially when that advice is legally correct but socially controversial.
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:2607. 20461v1 Announce Type: cross Abstract: Present implementations of artificial intelligence (AI) ethics do not adequately take feelings, or affect, into account.
arXiv:2608. 20041v1 Announce Type: new Abstract: Research on the agency of advanced artificial intelligence (AI) systems focuses on agency as a normative concept and on the agency of particularly agentic AI systems.
The paper examines how the rise of AI capable of moral reasoning could reshape meta-ethics, traditionally focused on human ethics. It proposes a framework that identifies new questions about AI’s own ethics from both human and AI perspectives, dividing them into four domains. The author explores how existing meta-ethical theories might apply to these domains and argues that many human-centered formulations will need significant revision to accommodate AI.