Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing

TL;DR

This paper introduces a novel adversarial debiasing technique for deep classifiers that effectively reduces bias caused by spurious feature correlations, improving fairness and accuracy in critical applications.

Contribution

The paper proposes a new adversarial debiasing method that addresses bias from irrelevant features, with a mathematical proof of its superiority over existing techniques.

Findings

Outperforms state-of-the-art on benchmark datasets

Mathematically proven to be more effective

Reduces bias in real-world computer vision tasks

Abstract

Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a direct consequence of a bias in the training dataset, frequently caused by the co-occurrence of relevant features and irrelevant ones. To mitigate this issue, we require learning algorithms that prevent the propagation of bias from the dataset into the classifier. We present a novel adversarial debiasing method, which addresses a feature that is spuriously connected to the labels of training images but statistically independent of the labels for test images. Thus, the automatic identification of relevant features during training is perturbed by irrelevant features. This is the case in a wide range of bias-related problems for many computer vision tasks, such as automatic skin cancer detection or driver assistance. We argue by a mathematical proof that our approach is superior to existing techniques for the abovementioned bias. Our experiments show that our approach performs better than state-of-the-art techniques on a well-known benchmark dataset with real-world images of cats and dogs.