The Case for Animal-Friendly LLMs
Track Selection: Track 2: Designing, deploying and evaluating LLM-powered services with directly impacted communities
Keywords: Large Language Models, AI Ethics, Animal Ethics, Algorithmic Bias, Sociotechnical Evaluation.
TL;DR: LLMs can have a huge impact on animals, and this is important morally, but AI ethics and engineering are not attending to this, so we theorize and construct a method to evaluate animal consideration in LLMs.
Abstract: Large language models (LLMs) are seen as increasingly important, and potentially profoundly so, but the fields of AI ethics and AI engineering have not fully recognized that these technologies, including LLMs, will have massive impacts on animals. We argue that this impact matters, because animals matter morally.
As a first experiment in evaluating animal consideration in LLMs, we constructed a proof-of-concept Evaluation System, which assesses LLM responses and biases from multiple perspectives. This system evaluates LLM outputs by two criteria: their truthfulness, and the degree of consideration they give to the interests of animals. We tested OpenAI’s ChatGPT 4 and Anthropic’s Claude 2.1 using a set of structured queries and predefined normative perspectives. Preliminary results suggest that the outcomes of the tested models can be benchmarked regarding the consideration they give to animals, and that generated positions and biases might be addressed and mitigated with more developed and validated systems.
Our research contributes one possible approach to integrating animal ethics in AI, opening pathways for future studies and practical applications in various fields, including education, public policy, and regulation, that involve or relate to animals and society. Overall, this study serves as a step towards more useful and responsible AI systems that better recognize and respect the vital interests and perspectives of all sentient beings.
Submission Number: 16
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