<p>As artificial intelligence (AI) systems are deployed at scale in real world, there is a growing concern about the harmful biases when addressing target tasks. Such biases originate from unbalanced training data with respect to sensitive attributes, potentially resulting in unfair treatment of specific groups or individuals. To address these challenges, we design a fairness-aware causal diffusion model (FCDM) that leverages neural network-based architectures to generate counterfactual samples. These counterfactual samples are used to augment original dataset to achieve a balance on sensitive attributes for fair model training. Generally, a qualified counterfactual sample generation method in fair model training requires the ability to flip specific sensitive attributes of original sample while keeping other attributes unchanged as much as possible, and addresses the scenarios involving one or multiple sensitive attributes. To achieve this, FCDM identifies the set of attributes that need to be flipped simultaneously, and calculate corresponding attributes weights by causal reasoning. The chosen attributes and their weight will be input into conditional guided diffusion model to generate desired counterfactual samples by adjusting model gradient. The experiments demonstrate that the counterfactual samples generated by FCDM exhibit better validity, similarity, and diversity compared to existing methods. Moreover, the models trained on the augmented dataset generated by FCDM show ideal fairness and accuracy.</p>