Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …

While AI has benefited humans, it may also harm humans if not appropriately developed.
The priority of current HCI work should focus on transiting from conventional human …

As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …

Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …

We introduce four principles for explainable artificial intelligence (AI) that comprise
fundamental properties for explainable AI systems. We propose that explainable AI systems …

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

The onus on the average person is greater than ever before to make sense of large amounts
of readily accessible quantitative information, but the ability and confidence to do so are …

Abstract Generative Artificial Intelligence is set to revolutionize healthcare delivery by
transforming traditional patient care into a more personalized, efficient, and proactive …

Demands to manage the risks of artificial intelligence (AI) are growing. These demands and
the government standards arising from them both call for trustworthy AI. In response, we …