Unintended bias in Machine Learning can manifest as systemic differences in performance
for different demographic groups, potentially compounding existing challenges to fairness in …

In this work, we present a framework to measure and mitigate intrinsic biases with respect to
protected variables-such as gender-in visual recognition tasks. We show that trained models …

An increasing number of works in natural language processing have addressed the effect of
bias on the predicted outcomes, introducing mitigation techniques that act on different parts …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

Language can be used as a means of reproducing and enforcing harmful stereotypes and
biases and has been analysed as such in numerous research. In this paper, we present a …

Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed
networks in various web-based applications such as social recommendation and web …

Fairness in deep learning has attracted tremendous attention recently, as deep learning is
increasingly being used in high-stake decision making applications that affect individual …

Differentially private stochastic gradient descent (DP-SGD) adds noise to gradients in back-
propagation, safeguarding training data from privacy leakage, particularly membership …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

Recent research has shown that large-scale pretrained language models, specifically
transformers, tend to exhibit issues relating to racism, sexism, religion bias, and toxicity in …