Developing Animal-Friendly AI (AI and Animal Welfare)

The importance and urgency of developing animal friendly AI

This collection of articles highlights the lack of animal consideration in AI and underscores the importance of including animals in AI ethics.

This paper is particularly valuable for its focus on embedding animal welfare directly into the development and governance of AI systems. It emphasizes the need for explicit representation of animals in AI decision-making processes, risk assessments, and corporate policies. By proposing real-world mechanisms such as incorporating animals into mission statements and governance structures, it shifts the conversation from abstract ethics to concrete, actionable frameworks. The paper also highlights the necessity of building a specialized community at the intersection of AI safety and animal advocacy, ensuring long-term integration of animals into AI development for sustainable outcomes.

This article discusses the neglected risk that AI systems may pose existential and suffering threats to nonhuman animals. It argues that the AI value alignment problem has focused only on human values, which is speciesist. The paper contends that AI should also be aligned with animal values, examines challenges in value extraction and aggregation, and explores approaches for integrating animal values into AI systems.

This paper argues that the field of AI ethics should expand to give nonhuman animals more consistent and extensive moral consideration, moving beyond the current state where they are often ignored. While it emphasizes the need to include nonhumans in ethical discussions, it does not specify how to balance or weigh these interests.

This research article discusses a project aimed at developing AI models that incorporate animal welfare considerations. It builds on previous work to refine evaluation methods, aiming to make AI systems more aligned with animal-friendly values while raising awareness of AI's potential in animal advocacy.

This paper introduces Sentientist Coherent Extrapolated Volition (SCEV) as an alternative to traditional value-learning models like CEV, which focus only on human preferences. SCEV emphasizes including all sentient beings—non-human animals and potential digital minds—in AI design. By integrating the interests of all sentient beings, SCEV aims to prevent catastrophic outcomes and reduce suffering risks associated with excluding non-humans from AI alignment.

This paper contends that AI should be designed with ethical considerations for animals, as AI systems can directly and indirectly affect animal welfare. The authors propose a framework to assess how large language models incorporate animal-friendly ethics, examining models like GPT-4 and Claude 2.1. They suggest that AI developers and policymakers integrate these concerns to benefit animals globally.

This paper introduces the concept of 'speciesist bias' in AI, highlighting how AI fairness research often overlooks discrimination against animals. Through normative analysis and case studies, it examines biases in AI systems like image recognition and language models. The findings show that speciesist patterns in datasets reinforce biases in AI applications, perpetuating and normalizing violence against animals.

This research paper evaluates moral decision-making in AI models across multiple languages, using trolley problem scenarios to study human versus animal preferences. The study finds that large language models generally prioritize saving humans over animals, though this preference varies across cultures.