Sep 2026· AI and Ethics· Vol 6· 0 citations· 39 references
TL;DR
It is concluded that, while AI may be considered a valuable tool for supporting human moral deliberation, it cannot by itself serve as an expert moral decision-maker.
The Stoic concept of the hegemonikon—the capacity for assent that directs the mind—is used as a framework for understanding intellectual creation and decision-making, clarifying when AI supports human judgment and when it replaces decision-making authority.
Christos A. Koutsotasios, Elias Vavouras· Dianoesis· 0 citations
The article argues that many contemporary AI alignment practices risk a mistaken assimilation of moral agency to statistical learning. Techniques such as reinforcement learning from human feedback and constitutional AI often treat morality as a behavioral function that can be approximated from human discourse, behavior...
An ethics of non-agentive AI is sketched: the authors should see these systems as powerful, instrument-like extensions of human cognition, not as knowers in their own right, and design institutions, interfaces and norms of trust accordingly.
Neumann Saskia Janina· Digital Society· 0 citations
As LLMs take on roles requiring moral advice, understanding how they attribute moral agency becomes critical. Humans possess moral agency, the capacity to make ethically guided decisions and bear responsibility for their consequences, a well-established construct in moral psychology. Yet as artificial agents (AAs) such...
F. Mansilla, Aloysius Y. F. Tok, B. Guellaï et al.· 0 citations
It is argued that once moral trustworthiness is set aside, the central normative question shifts from whether AI systems can be trusted to how their epistemic influence on human decision-making ought to be justified and governed.
John Dorsch, Maximilian Moll, Ophélia Deroy· Philosophy & Technology· 0 citations
It is found that at least for the time being, explicit normative instructions are not fully able to realign AI advice with the normative convictions of the population, or the legislator deciding on its behalf.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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