Portable Causal Fairness Across Synthetic Data Generator Families
Read the original on arXiv Machine Learning →The paper demonstrates that causal fairness mechanisms can be applied across a wide range of synthetic data generators, including marginal‑based, GAN, and diffusion models, each with differentially private variants. By porting three fairness definitions to nine generators and testing them on Adult and COMPAS datasets, the authors show that the causal diffusion backbone consistently produces the fairest data releases while maintaining high fidelity. The fairness cuts have minimal impact on data quality, costing downstream classifiers only about $0.07$ to $0.15$ AUC on average, and adding privacy guarantees does not reduce fairness.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.