Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare. However, they often suffer from fairness issues due to inherent biases in node attributes and graph structure, leading to unfair predictions. To address these challenges, we propose a novel GNN framework, DAB-GNN, that Disentangles, Amplifies, and deBiases attribute, structure, and potential biases in the GNN mechanism. DAB-GNN employs a disentanglement and amplification module that isolates and amplifies each type of bias through specialized disentanglers, followed by a debiasing module that minimizes the distance between subgroup distributions to ensure fairness. Extensive experiments on five datasets demonstrate that DAB-GNN significantly outperforms ten state-of-the-art competitors in terms of achieving an optimal balance between accuracy and fairness.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Most studies introducing AI models for COVID-19 diagnosis and prognostication exhibit systematic errors that make them unusable in most clinical settings. However, there remain opportunities for machine learning to assist front-line workers during the COVID-19 pandemic, and the steps we take now will leave us better in the future.

The pace of technology is changing over the years, as remarked in the 20th edition of Accenture's Tech Vision 2020. Released earlier this year, this report is based on two decades of study, supported by tech trends and their evolution over time. To keep with the competition, it is imperative that enterprises need to embrace technologies concerning their existing investment portfolios. The report says that artificial intelligence and robotics will be among the key technology trends redefining business parlance over the next three years. The need of the hour for enterprises is to answer the call to rebuild business and technology models from the ground up, keeping in tandem stakeholders' expectations a priority.