Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

To achieve high accuracy in machine learning (ML) systems, practitioners often use complex
“black-box” models that are not easily understood by humans. The opacity of such models …

There have been several research works proposing new Explainable AI (XAI) methods
designed to generate model explanations having specific properties, or desiderata, such as …

Stroke presents a formidable global health threat, carrying significant risks and challenges.
Timely intervention and improved outcomes hinge on the integration of Explainable Artificial …

In recent times, AI and UAV have progressed significantly in several applications. This article
analyzes applications of UAV with modern green computing in various sectors. It addresses …

Most existing evaluations of explainable machine learning (ML) methods rely on simplifying
assumptions or proxies that do not reflect real-world use cases; the handful of more robust …

AI-driven decision-making systems are becoming instrumental in the public sector, with
applications spanning areas like criminal justice, social welfare, financial fraud detection …

Increasingly, laws are being proposed and passed by governments around the world to
regulate artificial intelligence (AI) systems implemented into the public and private sectors …

Explainable artificial intelligence (XAI) is a burgeoning concept. It is gaining prominence as
an approach to better understand how artificial intelligence solutions' outputs can improve …

This paper explores how machine learning (ML) can enhance both policy-making and policy
evaluation by providing advanced tools for data analysis, predictive modeling, and …