Research in machine learning fairness has historically considered a single binary
demographic attribute; however, the reality is of course far more complicated. In this work …

Fairness in visual recognition is becoming a prominent and critical topic of discussion as
recognition systems are deployed at scale in the real world. Models trained from data in …

Machine learning-based (ML) systems are being largely deployed since the last decade in a
myriad of scenarios impacting several instances in our daily lives. With this vast sort of …

Demographic parity is the most widely recognized measure of group fairness in machine
learning, which ensures equal treatment of different demographic groups. Numerous works …

Variances in ad impression outcomes across demographic groups are increasingly
considered to be potentially indicative of algorithmic bias in personalized ads systems …

As the social impact of visual recognition has been under scrutiny, several protected-
attribute balanced datasets emerged to address dataset bias in imbalanced datasets …

Fairness has become a crucial aspect in the development of trustworthy machine learning
algorithms. Current fairness metrics to measure the violation of demographic parity have the …

Increases in the deployment of machine learning algorithms for applications that deal with
sensitive data have brought attention to the issue of fairness in machine learning. Many …

Amassing large-scale datasets used to train machine learning algorithms often includes
crowd-sourcing or web scraping. The data resulting from these approaches can carry …

Applications that deal with sensitive information may have restrictions placed on the data
available to a machine learning (ML) classifier. For example, in some applications, a …