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Diffusion models have shown their effectiveness in generation tasks bywell-approximating the underlying probability distribution. However, diffusionmodels are known to suffer from an amplified inherent bias from the trainingdata in terms of fairness. While the sampling process of diffusion models canbe controlled by conditional guidance, previous works have attempted to findempirical guidance to achieve quantitative fairness. To address thislimitation, we propose a fairness-aware sampling method called\textit{attribute switching} mechanism for diffusion models. Without additionaltraining, the proposed sampling can obfuscate sensitive attributes in generateddata without relying on classifiers. We mathematically prove and experimentallydemonstrate the effectiveness of the proposed method on two key aspects: (i)the generation of fair data and (ii) the preservation of the utility of thegenerated data.

Fair Sampling in Diffusion Models through Switching Mechanism