Targeted Data Augmentation for bias mitigation

TL;DR

This paper introduces Targeted Data Augmentation, a novel method that mitigates biases in AI models by intentionally inserting biases during training, leading to significant bias reduction with minimal impact on accuracy.

Contribution

It presents a new bias mitigation technique using targeted data augmentation and provides the first annotated datasets for bias analysis in clinical and facial data.

Findings

Biases related to frame, ruler, and glasses significantly affect models.

Random bias insertion during training reduces bias measures two- to fifty-fold.

Bias mitigation achieved with negligible increase in error rate.

Abstract

The development of fair and ethical AI systems requires careful consideration of bias mitigation, an area often overlooked or ignored. In this study, we introduce a novel and efficient approach for addressing biases called Targeted Data Augmentation (TDA), which leverages classical data augmentation techniques to tackle the pressing issue of bias in data and models. Unlike the laborious task of removing biases, our method proposes to insert biases instead, resulting in improved performance. To identify biases, we annotated two diverse datasets: a dataset of clinical skin lesions and a dataset of male and female faces. These bias annotations are published for the first time in this study, providing a valuable resource for future research. Through Counterfactual Bias Insertion, we discovered that biases associated with the frame, ruler, and glasses had a significant impact on models. By randomly introducing biases during training, we mitigated these biases and achieved a substantial decrease in bias measures, ranging from two-fold to more than 50-fold, while maintaining a negligible increase in the error rate.