We are developing AI-powered tools to support the application of the Welfare Footprint Framework (WFF) and make welfare analysis more structured and scalable.
Helps users understand and apply the Welfare Footprint Framework correctly, including terminology, modules, causal structure, Cumulative Pain and Cumulative Pleasure, and appropriate use of WFF concepts.
Reviews animal welfare research proposals for decision relevance, study design, welfare interpretation, statistical robustness, and red-team risks. It suggests the WFF only when structured welfare quantification is genuinely relevant.
Maps production systems and environmental conditions, structuring the Circumstances animals are exposed to.
Helps map the causal chain of welfare from Circumstances to Affective Experiences.
Supports full welfare analysis, including Pain-Tracks and Pleasure-Tracks.
Explores differences in welfare-relevant capacities across species.
Applies epidemiological reasoning to welfare, including prevalence and exposure.
Explores affective states and consciousness through neuroscience and philosophical reasoning, helping users think more deeply about the nature of animal experiences.
Consumer-facing prototype connecting welfare analysis to food choices.
