Abstract: This paper proposes a method for multi-sensitive attribute data debiasing,leveraging adversarial learning and autoencoder to eliminate correlations between sensitive and non-sensitive attributes,minimize the impact on model accuracy when striving for fairness,and address the issue of multi-sensitive attribute debiasing.In addressing multi-sensitive attribute debiasing,this method groups based on the combined values of multiple sensitive attributes,enhancing the fairness of each group's predictions by eliminating group correlations with these sensitive attribute combinations.To eliminate correlations between sensitive and non-sensitive attributes,an adversarial training approach is employed,utilizing auto-encoders alongside networks predicting sensitive attributes.This training effectively uncovers and eliminates latent sensitive attribute-related information within the groups,signi-ficantly reducing bias while retaining data utility.To mitigate the impact on model accuracy from striving for fairness and optimize the balance between accuracy and fairness,a prediction network is introduced.Its loss function is used as a constraint to enhance the encoder's ability to extract information,ensuring more precise capture of key information during data encoding and preventing excessive sacrifice of predictive performance during the debiasing process.Data debiasing experiments on three real datasets are conducted,applying the encoded data to logistic regression models.The fairness improvements range from 50.5% to 84%,validating the effectiveness of the debiasing method.Considering fairness,accuracy,and their balance,this debiasing method outperforms other debiasing algorithms.

Key words: Data depolarization, Machine learning, Adversarial learning, Auto-encoder