The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

The need for interpretable and accountable intelligent systems grows along with the
prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Explainable Artificial Intelligence (XAI) is an emerging research field bringing
transparency to highly complex and opaque machine learning (ML) models. Despite the …

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 …

For an explanation of a deep learning model to be effective, it must provide both insight into
a model and suggest a corresponding action in order to achieve some objective. Too often …

Recent advances in deep learning have improved the performance of many Natural
Language Processing (NLP) tasks such as translation, question-answering, and text …

Explainable AI (XAI) refers to techniques that provide human-understandable insights into
the workings of AI models. Recently, the focus of XAI is being extended towards Large …

Machine learning models have been successfully applied for analysis of skin images.
However, due to the black box nature of such deep learning models, it is difficult to …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …