The paper extends causal fairness methodology from standard supervised ML (where you learn a single predictor) to generative AI, where models can sample from arbitrary conditionals and implicitly construct beliefs about all causal mechanisms, not just a single outcome. The author formalizes causal fairness for generative AI, derives new decomposition results that quantify fairness impacts along (a) different causal pathways and (b) the replacement of real-world mechanisms by the generative model's mechanisms, establishes identification conditions, and introduces efficient estimators. Empirical analysis demonstrates race and gender bias in large language models across datasets.
Main takeaways:
- Generative AI fairness is fundamentally different from standard ML fairness: generative models construct beliefs about all causal mechanisms, not just a single predictor.
- The paper unifies causal fairness for generative AI and standard ML under a common theoretical framework.
- New causal decomposition results enable granular quantification of bias along causal pathways and due to mechanism replacement.
- Identification conditions and efficient estimators are provided for causal fairness quantities.
- Empirical analysis reveals race and gender bias in LLMs across different datasets.