Deep Learning Model based on Autoencoder for Reducing Algorithmic Bias of Gender

Jin-Young Kim, Sung-Bae Cho

http://doi.org/10.5626/JOK.2019.46.8.721

Algorithmic bias is a discrimination that is reflected in the model by a bias in data or combination of characteristics of model and data in the algorithm. In recent years, it has been identified that the bias is not only present but also amplified in the deep learning model; thus, there exists a problem related to bias elimination. In this paper, we analyze the bias of the algorithm by gender in terms of bias-variance dilemma and identify the cause of bias. To solve this problem, we propose a deep auto-encoder based latent space matching model. Based on the experimental results, it is apparent that the algorithm bias in deep learning is caused by difference of the latent space for each protected feature in the feature extraction part of the model. A model proposed in this paper achieves the low bias by reducing the differences in extracted features by transferring data with different gender characteristics to the same latent space. We employed Equality of Odds and Equality of Opportunity as a quantitative measure and proved that proposed model is less biased than the previous model. The ROC curve shows a decrease in the deviation of the predicted values between the genders.