As AI large language models (LLMs) become increasingly embedded in everyday technologies, should we be concerned about their capacity to influence human beliefs - particularly in the moral domain? Being persuaded ...
AI language model rivals expert ethicist in perceived moral expertise
People view AI as possessing expertise across various fields, but the perceived quality of AI-generated moral expertise remains uncertain. Recent work suggests that large language models (LLMs) perform well on tasks designed to assess moral alignment, reflecting moral judgments with relatively high accuracy. As LLMs are increasingly employed in decision-making roles, there is a growing expectation for them to offer not just aligned judgments but also demonstrate sound moral reasoning. Here, we advance work on the Moral Turing Test and find that Americans rate ethical advice from GPT-4o as slightly more moral, trustworthy, thoughtful, and correct than that of the popular New York Times advice column, The Ethicist. Participants perceived GPT models as surpassing both a representative sample of Americans and a renowned ethicist in delivering moral justifications and advice, suggesting that people may increasingly view LLM outputs as viable sources of moral expertise. This work suggests that people might see LLMs as valuable complements to human expertise in moral guidance and decision-making. It also underscores the importance of carefully programming ethical guidelines in LLMs, considering their potential to influence users’ moral reasoning.
A Rational Analysis of the Effects of Sycophantic AI
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We...
AI, Ethics, and Society — Home
AI Large Language Model Training: The Potential Risks of Ideological Skewing — PSG Consulting
LLMs (AI Large Language Models) have become part of everyday life. Systems such as ChatGPT, Claude, Gemini, Meta AI (Llama) and X.ai's Grok handle billions of interactions daily. They increasingly shape what information people encounter and in what order, subtly deciding what's important and even what is true, sometimes without users realizing it. Because LLMs wield growing power over information exposure, it is vital to recognize the political and ideological structures at multiple stages of their design, and to identify manipulation risks.
Taking AI Welfare Seriously
In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood, i.e. of AI systems with their own interests and moral significance, is no longer an issue only for sci-fi or the distant future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously. We also recommend three early steps that AI companies and other actors can take: They can (1) acknowledge that AI welfare is an important and difficult issue (and ensure that language model outputs do the same), (2) start assessing AI systems for evidence of consciousness and robust agency, and (3) prepare policies and procedures for treating AI systems with an appropriate level of moral concern. To be clear, our argument in this report is not that AI systems definitely are, or will be, conscious, robustly agentic, or otherwise morally significant. Instead, our argument is that there is substantial uncertainty about these possibilities, and so we need to improve our understanding of AI welfare and our ability to make wise decisions about this issue. Otherwise there is a significant risk that we will mishandle decisions about AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly caring for AI systems that do not.
Large-scale harms to AI moral patients: a typology of risk factors
If AI moral patients are mass-produced, then then they will face risks of large-scale harms. This post outlines some factors that could contribute to such risks.
[Keynote 05] Unlocking Social Intelligence in AI Agents
People & Technology
To understand AI’s effect on moral character, ethicist Kwame Anthony Appiah goes back to John Stuart Mill, and the idea that people are shaped by their choices.
AI Ethics Class
The Future of AI
The Parents’ Paradox: AI, Ethics, and the Limits of Machine Morality This post is based on a talk I gave at The AI & Automation Conference in London on February 25, 2026, and my slides. A…
AI learns language from skewed sources. That could change how we humans speak – and think | Bruce Schneier
Large language models aren’t trained on real-life conversations. As we encounter their language, it could affect our own
2026 Cosmos HAI Lab Lecture with Jack Clark, Co-founder of Anthropic
The Thoughts The Civilized Keep
The hype around a new AI language generator reveals the sterility of mainstream thinking on AI today — and indeed on how we think about thinking itself.
We Asked the ‘Future of Truth’ Author to Explain How He Used AI. It Didn’t Go Well
A book about how AI shapes perceptions of reality came under fire for using AI-generated quotes. Its problems go beyond that.
[Keynote 03] Simulating Emergent LLM Social Behaviors in Multi Agent Systems
I think it’s telling that people very interested in AI (like Eugene and myself) still have no interest in using it as a proxy for human communication. “Being a good writer” is not the same thing as having social agency, and making the models even better at writing won’t change that.
Eugene Vinitsky 🍒
When you deploy heavily LLM text, you currently have no way to prove that you actually read it and therefore cannot convince me to read it
