The Animal Question in AI: Speciesism, Bias, and Animal Welfare Policy
This session, featuring Sky Kehan Sheng, PhD candidate at the University of British Columbia, explores how artificial intelligence systems represent and affect non human animals.
AI fairness research has focused mainly on human harms. Increasingly, scholars are asking whether AI also reproduces speciesist bias. From training data to generative image models, AI systems can reinforce narratives that normalize harm toward animals and misrepresent practices such as industrial farming. This discussion will explore:
How AI systems encode and reproduce speciesist bias
The limits of current AI ethics frameworks that focus primarily on human harms
What animal welfare perspectives contribute to debates on responsible AI governance
The reading group brings together UBC students from all academic levels with faculty members to engage collectively with scholarship in AI policy. No prior technical background in AI or policy is required.
Sky (Kehan) Sheng is a PhD candidate in the Animal Welfare Program at the University of British Columbia whose research examines the intersection of animal welfare science, data science, and AI ethics. Her work investigates how machine learning and generative AI systems represent animals and agricultural practices. In recent research, she has examined how text to image models often romanticize livestock farming and obscure the realities of modern industrial production systems. Sheng also studies how AI technologies can be used to improve animal welfare monitoring, including automated welfare assessment systems for livestock and the role of animal welfare scientists in developing ethical AI based monitoring tools.