Unbiased Model Prediction Without Using Protected Attribute Information

Abstract

The problem of bias persists in the deep learning community as models continue to provide disparate performance across different demographic subgroups. Therefore, several algorithms have been proposed to improve the fairness of deep models. However, a majority of these algorithms utilize the protected attribute information for bias mitigation, which severely limits their application in real-world scenarios. To address this concern, we have proposed a novel algorithm, termed as \textbf{Non-Protected Attribute-based Debiasing (NPAD)} algorithm for bias mitigation, that does not require the protected attribute information. The proposed NPAD algorithm utilizes the auxiliary information provided by the non-protected attributes to optimize the model for bias mitigation. Further, two different loss functions, \textbf{Debiasing via Attribute Cluster Loss (DACL)} and \textbf{Filter Redundancy Loss (FRL)} have been proposed to optimize the model for fairness goals. Multiple experiments are performed on the LFWA and CelebA datasets for facial attribute prediction, and a significant reduction in bias across different gender and age subgroups is observed.

Figures (11)

• Figure 1: Illustration of model training using (a) conventional approaches, (b) conventional debiasing approaches, and (c) proposed Non-Protected Attribute-based Debiasing (NPAD) algorithm. The proposed algorithm does not require the protected attribute information for bias mitigation.
• Figure 2: Block diagram illustrating model training using the proposed NPAD algorithm. In the first stage, disparity is computed by evaluating the model trained for predicting attribute $\mathbf{A}_i$ across the classes of other non-protected attributes. Next, the non-protected attributes are selected based on the disparity and independence. In the second stage, model is optimized using the proposed DACL and FRL functions for bias mitigation.
• Figure 3: A toy example of the feature visualization of a model trained for shape prediction on the testing set. Solid and Dotted represent classes of the non-protected attribute and Color represents the protected attribute. (a) Shows the biased feature representations of the model (trained using conventional approaches). (b) Shows the unbiased feature representations of the model (trained using the proposed NPAD algorithm). (c) Shows the unbiased feature representations of the model across the unknown protected attribute (assuming the non-protected attribute is fully correlated with the protected attribute).
• Figure 4: Sample images of the (a) LFWA huang2008labeled and (b) CelebA liu2015faceattributes datasets. Images of both datasets are collected in unconstrained environmental settings with variation in pose, illumination, and the degree of occlusion.
• Figure 5: t-SNE visualization using BMT (top row) and the proposed NPAD algorithm (bottom row) corresponding to attributes 'Big Nose' and 'Black Hair' of the LFWA dataset.