Soenke Ziesche

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.

The AI value alignment problem has now been acknowledged as essential for AI safety as well as very hard. In this chapter we argue that critical parameters are neglected in AI value alignment research, which are consciousness and qualia. The AI value alignment problem is about ensuring that AI systems pursue goals, which are aligned with the interests of moral patients. Briefly summarized, prevalent human interests are to foster happiness and pleasure and to avoid pain; thus, experiences perceived through consciousness and qualia. Therefore, AI systems need not only to understand qualia and consciousness, but also their precious significance in order to be truly aligned with human interests as well as with the interests of other sentient beings. Death constitutes for humans the end of consciousness, thus, the termination of the opportunity to experience happiness and pleasure. Therefore, AI systems must not kill sentient beings. In this chapter we describe the importance of incorporating consciousness and qualia research to AI value alignment research as well as the potential feasibility of such efforts due to developments in neurotechnology. Concluding, we offer recommendations outlining such undertaking as a compulsory component of the ongoing mammoth task to reduce the x- as well as the s-risks posed by a potential superintelligence.

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.

This paper introduces the concept of "unconditional basic meaning" as a digital public good, proposing that governments provide citizens with access to AI-powered virtual worlds that foster personal growth, creativity and fulfilment. As AI-supported automation increasingly displace human jobs and other daily chores, many citizens risk losing their sense of purpose or ikigai, referred to as i-risks, while having much more spare time at hand. To address this challenge, the paper argues that governments should invest in digital public goods that prioritize citizens' well-being and purpose. By providing unconditional basic meaning, governments can also help create a more just and equitable society where everyone has access to a sense of purpose and fulfilment, regardless of their background or socioeconomic status. This would not be the case if the corporate sector was to provide digital meaning. The paper also addresses a number of potential objections to this approach and concludes with specific recommendations for governments to implement AI-powered virtual worlds as a digital public good.

While the urgent need for ethics for the thriving field of AI has been acknowledged, currently Western approaches to AI ethics are prevalent. This constitutes a problem because, on the one hand, these approaches tend to reflect the values of the regions where they are originating from, on the other hand, not all values are universal. This form of digital neo-colonialism ought to be prevented. As a step in this direction this article presents ten selected concepts of non-Western approaches to AI ethics and analyses their originality as well as potential combability with the Western approaches. Based on this, the article concludes with a recommendation to merge Western and non-Western approaches towards universal AI ethics as far as they are compatible and to attempt to reconcile those aspects, which appear incompatible.

Artificial intelligence (AI) is the algorithms designed to make decisions, often using big real-time data to perform activities that at times go beyond human capabilities. Given the increasing gap in agricultural demand and supply worldwide, further widened by the COVID-19 pandemic (The pandemic has derailed the progress towards Sustainable Development Goals (SDGs) further off the track. The SDG financing gap per annum widened from USD 2.5 trillion to around USD 4.2 trillion), it necessitates innovative and cost-effective approaches to agriculture. AI has begun producing innovative technological solutions and data-driven insights to farming which gives confidence that it can be used to mitigate challenges around sustainable agricultural practices and facilitate getting SDGs back on track. In agriculture, AI has demonstrated immense potential in achieving enhanced productivity and improving the existing supply chains, delivery systems and market value/better pricing in both developed and developing countries for better utilisation of the produce.Several innovative uses of AI in agriculture have emerged worldwide, promising to advance farm productivity while improving sustainability and livelihoods at the same time. However, many of these experiments/pilots exist in silos. Due to this fragmented approach, a comprehensive understanding of how successful the use of AI has been in agriculture and what shortcomings or challenges were faced in some of these technological implementations has not been well evaluated. This chapter, therefore, assesses the pressing reasons to use innovative and cost-effective digital interventions like AI for SDGs in the agriculture sector. The paper then identifies the challenges in designing a successful AI programme and explores the potential of multi-stakeholder partnerships in this context.

Ikigai is a Japanese concept, which, in brief, refers to the “reason or purpose to live”. I-risks have been identified as a category of risks complementing x- risks, i.e., existential risks, and s-risks, i.e., suffering risks, which describes undesirable future scenarios in which humans are deprived of the pursuit of their individual ikigai. While some developments in AI increase i-risks, there are also AI-driven virtual opportunities, which reduce i-risks by increasing the space of potential ikigais, largely due to developments in generative AI, virtual worlds as well as AI-driven hyper-personalization. The purpose of this paper is to present a first attempt to map the potential AI-driven virtual hyper-personalised ikigai universe. Moreover, challenges and further ideas are presented.

The problem of personal identity is a longstanding philosophical topic albeit without final consensus. In this article the somewhat similar problem of AI identity is discussed, which has not gained much traction yet, although this investigation is increasingly relevant for different fields, such as ownership issues, personhood of AI, AI welfare, brain–machine interfaces, the distinction between singletons and multi-agent systems as well as to potentially support finding a solution to the problem of personal identity. The AI identity problem analyses the criteria for two AIs to be considered the same at different points in time. Two approaches to tackle the problem are proposed: One is based on the personal identity problem and the concept of computational irreducibility, while the other one applies multi-factor authentication to the AI identity problem. Also, a range of scenarios is examined regarding AI identity, such as replication, fission, fusion, switch off, resurrection, change of hardware, transition from non-sentient to sentient, journey to the past, offspring and identity change.

This article is about a specific, but so far neglected peril of AI, which is that AI systems may become existential as well as causing suffering risks for nonhuman animals. The AI value alignment problem has now been acknowledged as critical for AI safety as well as very hard. However, currently it has only been attempted to align the values of AI systems with human values. It is argued here that this ought to be extended to the values of nonhuman animals since it would be speciesism not to do so. The article focuses on the two subproblems—value extraction and value aggregation—discusses challenges for the integration of values of nonhuman animals and explores approaches to how AI systems could address them.

Various authors have argued that in the future not only will it be technically feasible for human minds to be transferred to other substrates, but this will become, for most humans, the preferred option over the current biological limitations. It has even been claimed that such a scenario is inevitable in order to solve the challenging, but imperative, multi-agent value alignment problem. In all these considerations, it has been overlooked that, in order to create a suitable environment for a particular mind – for example, a personal universe in a computational substrate – numerous other potentially sentient beings will have to be created. These range from non-player characters to subroutines. This article analyzes the additional suffering and mind crimes that these scenarios might entail. We offer a partial solution to reduce the suffering by imposing on the transferred mind the perception of indicators to measure potential suffering in non-player characters. This approach can be seen as implementing literal empathy through enhanced cognition.

Two interconnected surveys are presented, one of artifacts and one of designometry. Artifacts are objects, which have an originator and do not exist in nature. Designometry is a new field of study, which aims to identify the originators of artifacts. The space of artifacts is described and also domains, which pursue designometry, yet currently doing so without collaboration or common methodologies. On this basis, synergies as well as a generic axiom and heuristics for the quest of the creators of artifacts are introduced. While designometry has various areas of applications, the research of methods to detect originators of artificial minds, which constitute a subgroup of artifacts, can be seen as particularly relevant and, in the case of malevolent artificial minds, as contribution to AI safety.