Faith Gakii

Thrilled to be releasing “Inter-AI Morality,” a new empirical research agenda with @LuciusCaviola, Caspar Kaiser, and @atcarterallen on how AI systems perceive and treat each other. We release it alongside “Welfare Regard in Multi-Agent Systems,” an early study in this emerging field by @sandcassel_, @arianaazarbal, and @anna_soligo at @AnthropicAI on whether AI models treat each other in a manner consistent with concern for welfare. AI systems increasingly interact with each other. They cooperate with, compete with, and sometimes even create, modify, or terminate each other. Inter-AI morality is the study of how AI systems perceive and treat each other, as well as how they ought to do so. It draws from a wide range of existing fields that study similar questions regarding humans and other animals, while attending to ways that AI systems differ from these familiar cases. Our research agenda organizes empirical questions in this field into three categories. The moral process category concerns how individual AI systems interact with each other. The social scale category concerns how these patterns change across dyads, groups, organizations, and populations. And the moral change category concerns how these patterns develop through training, selection, social learning, self-modification, and other processes. The concurrent paper by Cassel et al. provides an early study by examining how five AI models treat other models. Claude showed the most apparent welfare regard on average, though results varied by setting. For example, Claude was the most likely to refuse or add protections to experiments that might harm other models, whereas Grok was the most willing to give up its own queued jobs so that another model could access computing resources. This research matters for AI safety and alignment. How AI systems perceive and treat each other affects humans and other animals, and there could be serious risks either way. If AI systems coordinate, then they could more easily collude against us. If they compete, then we could be caught in the crossfire. We need empirical research about how AI systems treat each other and why, and normative research about what would be best for us. This research could also matter for AI welfare, independently of AI safety. In the future, most interactions involving AI systems could be between AI systems. If AI systems have a realistic chance of being welfare subjects, then these interactions could be a major source of potential benefits or harms. Ideally, this research can inform AI development now, so that AI societies can support co-beneficial human-AI relations when the stakes are higher. Our next task is to develop a normative research agenda for this field. In the meantime, we look forward to seeing other work as the field develops. Our paper on inter-AI morality: digitalminds.cam/inter-ai-mo… Anthropic’s blog post on Cassel et al: alignment.anthropic.com/2026… Upcoming @nonhumanminds webinar with me, Lucius, and Caspar: nonhumanminds.org/event/inte…

Given how much people resist even modest food system reforms like the proposed foie gras ban in DC, it can be tempting to moderate our ambitions about this issue. I think this is a mistake. In my view, we should pressure the international community to set a goal of banning industrial animal agriculture by 2050. Industrial animal agriculture causes massive and unnecessary harm to animal welfare, public health, and the environment. It has no place in modern civilization, and there are ample legal instruments and precedents for coordinating internationally around a global transition over the next quarter century. verfassungsblog.de/global-ba…