Deep neural networks are highly susceptible to learning biases in visual data. While various
methods have been proposed to mitigate such bias, the majority require explicit knowledge …

Deep image classifiers have been found to learn biases from datasets. To mitigate the
biases, most previous methods require labels of protected attributes (eg, age, skin tone) as …

Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and
nowadays they are used to solve an incredibly large variety of tasks. There are problems …

A critical problem in deep learning is that systems learn inappropriate biases, resulting in
their inability to perform well on minority groups. This has led to the creation of multiple …

Recent research has highlighted the vulnerabilities of modern machine learning based
systems to bias, especially towards segments of society that are under-represented in …

Identifying and mitigating bias in deep learning algorithms has gained significant popularity
in the past few years due to its impact on the society. Researchers argue that models trained …

Abstract Machine learning models are known to perpetuate and even amplify the biases
present in the data. However, these data biases frequently do not become apparent until …

Demographic bias is a significant challenge in practical face recognition systems. Several
methods have been proposed to reduce the bias, which rely on accurate demographic …

Abstract The failures of Deep Networks can sometimes be ascribed to biases in the data or
algorithmic choices. Existing debiasing approaches exploit prior knowledge to avoid …

Bias mitigation in image classification has been widely researched, and existing methods
have yielded notable results. However, most of these methods implicitly assume that a given …