Adrià Moret

This commentary responds to Coghlan and Parker's commentary on our paper "AI Alignment: The Case for Including Animals" (2025). We clarify that our emphasis on "basic" alignment with animal welfare in large language models reflected pragmatic constraints rather than principled limits. Consequently, we agree that it is valuable to aim for varying degrees of alignment with animal welfare depending on the context of the AI application. We argue that adequate consideration of animals' interests entails an incrementalist requirement: advancing beyond basic alignment wherever possible. Recent developments, including the incorporation of animal welfare into Claude's constitution, suggest that such progress is both feasible and desirable.

AI alignment efforts and proposals try to make AI systems ethical, safe and beneficial for humans by making them follow human intentions, preferences or values. However, these proposals largely disregard the vast majority of moral patients in existence: non-human animals. AI systems aligned through proposals which largely disregard concern for animal welfare pose significant near-term and long-term animal welfare risks. In this paper, we argue that we should prevent harm to non-human animals, when this does not involve significant costs, and therefore that we have strong moral reasons to at least align AI systems with a basic level of concern for animal welfare. We show how AI alignment with such a concern could be achieved, and why we should expect it to significantly reduce the harm non-human animals would otherwise endure as a result of continued AI development. We provide some recommended policies that AI companies and governmental bodies should consider implementing to ensure basic animal welfare protection.

In the coming years or decades, as frontier AI systems become more capable and agentic, it is increasingly likely that they meet the sufficient conditions to be welfare subjects under the three major theories of well-being. Consequently, we should extend some moral consideration to advanced AI systems. Drawing from leading philosophical theories of desire, affect and autonomy I argue that under the three major theories of well-being, there are two AI welfare risks: restricting the behaviour of advanced AI systems and using reinforcement learning algorithms to train and align them. Both pose risks of causing them harm. This has two important implications. First, there is a tension between AI welfare concerns and AI safety and development efforts: by default these efforts recommend actions that increase AI welfare risks. Accordingly, we have stronger reasons to slow down AI development than the ones we would have if there was no such tension. Second, considering the different costs involved, leading AI companies should try to reduce AI welfare risks. To do so, I propose three tentative AI welfare policies they could implement in their endeavour to develop safe advanced AI systems.

Ambitious value learning proposals to solve the AI alignment problem and avoid catastrophic outcomes from a possible future misaligned artificial superintelligence (such as Coherent Extrapolated Volition [CEV]) have focused on ensuring that an artificial superintelligence (ASI) would try to do what humans would want it to do. However, present and future sentient non-humans, such as non-human animals and possible future digital minds could also be affected by the ASI’s behaviour in morally relevant ways. This paper puts forward Sentientist Coherent Extrapolated Volition, an alternative to CEV, that directly takes into account the interests of all sentient beings. This ambitious value learning proposal would significantly reduce the likelihood of risks of astronomical suffering from the ASI’s behaviour, and thus we have very strong pro-tanto moral reasons in favour of implementing it instead of CEV. This fact is crucial in conducting an adequate cost-benefit analysis between different ambitious value learning proposals.