Prior work has shown that Visual Recognition datasets frequently underrepresent bias
groups B (eg Female) within class labels Y (eg Programmers). This dataset bias can lead to …

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 …

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 …

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 …

Despite the impressive prediction ability, machine learning models show discrimination
towards certain demographics and suffer from unfair prediction behaviors. To alleviate the …

Neural networks are often prone to bias toward spurious correlations inherent in a dataset,
thus failing to generalize unbiased test criteria. A key challenge to resolving the issue is the …

The idealization of a static machine-learned model, trained once and deployed forever, is
not practical. As input distributions change over time, the model will not only lose accuracy …

The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive
features that happen to be correlated with the labels in the training data. For example, a …

Mitigating bias in machine learning systems requires refining our understanding of bias
propagation pathways: from societal structures to large-scale data to trained models to …

Recent research suggests that predictions made by machine-learning models can amplify
biases present in the training data. When a model amplifies bias, it makes certain …