This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

Importance Health care algorithms are used for diagnosis, treatment, prognosis, risk
stratification, and allocation of resources. Bias in the development and use of algorithms can …

Due to the progressive advancement of trustworthy machine learning algorithms, fairness in
recommender systems is attracting increasing attention and is often considered from the …

When deploying machine learning solutions, they must satisfy multiple requirements beyond
accuracy, such as fairness, robustness, or safety. These requirements are imposed during …

We live in a vast ocean of data, and deep neural networks are no exception to this. However,
this data exhibits an inherent phenomenon of imbalance. This imbalance poses a risk of …

Objective This study leverages the rich diversity of the All of Us Research Program (All of
Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer …

Although adversarial training (AT) has proven effective in enhancing the model's robustness
the recently revealed issue of fairness in robustness has not been well addressed ie the …

As the data-driven decision process becomes dominating for industrial applications, fairness-
aware machine learning arouses great attention in various areas. This work proposes …

Existing group fairness-aware training methods fall into two categories: re-weighting
underrepresented groups according to certain rules, or using regularization terms such as …

While model fairness improvement has been explored previously, existing methods
invariably rely on adjusting explicit sensitive attribute values in order to improve model …