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Fair Feature Distillation Using Teacher Models of Larger Architecture
http://doi.org/10.5626/JOK.2021.48.11.1176
Achieving algorithmic fairness is becoming increasingly essential for various vision applications. Although a state-of-the-art fairness method, dubbed as MMD-based Fair feature Distillation (MFD), significantly improved accuracy and fairness via feature distillation based on Maximum Mean Discrepancy (MMD) compared to previous works, MFD could be limitedly applied into when a teacher model has the same architecture as a student model. In this paper, based on MFD, we propose a systematic approach that mitigates unfair biases via feature distillation of a teacher model of larger architecture, dubbed as MMD-based Fair feature Distillation with a regressor (MFD-R). Throughout the extensive experiments, we showed that our MFD-R benefits from the use of the larger teacher compared to MFD as well as other baseline methods.
Deep Learning Model based on Autoencoder for Reducing Algorithmic Bias of Gender
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.
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