This article examines how AI, particularly Large Language Models (LLMs), could soon enable tackling the enormous challenge of systematically quantifying the main sources of suffering across humans and animals.

Two years ago, a post by James Özden and Neil Dullaghan in this forum, on Megaprojects for Animals, highlighted the significant gap in the evidence base to inform effective strategies for animal welfare. They estimated a growth rate of research to inform the animal welfare evidence base lower than 1% of the growth rate seen in global health. 

We argue that leveraging AI offers a promising solution to address this gap. Drawing inspiration on the success of AlphaFold in rapidly advancing the field of molecular biology, AI can similarly revolutionize the mapping and quantification of animal suffering, providing the much needed evidence to inform and optimize animal welfare interventions, policies and decision-making in general. Because it channels existing knowledge and scientific evidence in a systematic way to inform estimates of animal suffering, the Welfare Footprint Framework (WFF) is an analytical approach especially well-suited to make use of AI's capabilities.

Providing a glimpse of what might be possible in the near future with the use of AI, we introduce the Pain-Track AI Tool. This tool is designed to describe and quantify negative affective experiences in animals, offering a starting point for the description and quantification of any source of suffering. 

Building on this foundation, we also introduce the Pain Atlas Project, envisaged as a large-scale initiative aimed at comprehensively mapping and quantifying the primary sources of human and animal suffering. This project, which we anticipate could have an impact in terms of knowledge advancement comparable to AlphaFold's achievements in molecular biology, foresees three core components:

• Mapping of Suffering: a comprehensive analysis of the primary source of suffering endured by different species throughout their lives.
• Quantification of Suffering: using the Cumulative Pain metric to estimate the magnitude of suffering associated with each of the sources of suffering identified.
• Visualization of Suffering: use of visualization tools to construct a detailed and global landscape of suffering across species and living conditions, guiding decision-making and intervention strategies.

The description of the AI tool and Pain Atlas Project is available here. We invite everyone to provide feedback in this forum and discuss potential collaborations (feel free to also reach out to us at AI@welfarefootprint.org).