Artificial intelligence and machine learning are transforming the ability to monitor, assess, and respond to animal welfare across contexts.
AI and machine learning welfare applications represent the most significant expansion of monitoring capacity in the history of animal welfare science. The ability to assess welfare continuously, objectively, and at scale overcomes fundamental limitations of human observation. However, technology alone does not improve welfare — data must be acted upon by appropriately trained and motivated humans. The risk of AI welfare monitoring becoming a compliance-documentation exercise rather than a genuine welfare improvement tool requires careful system design and cultural change in animal agriculture. Validation against established welfare science is essential — AI must detect real welfare problems, not merely generate data.