Introduction

Abstract

In this opening chapter, we set the stage for the book by describing both the opportunities and risks that artificial intelligence (AI) presents as it becomes increasingly embedded in critical domains such as healthcare, education, transportation, energy, conservation, and security. We argue that researchers and practitioners have a dual responsibility: to push the boundaries of technical innovation while also ensuring that AI systems are deployed in ways that are transparent, fair, and accountable. We begin by explaining the book&s;s structure. In the first half, we share case studies where AI methods—ranging from optimization and reinforcement learning to game theory and deep learning-are applied to concrete societal problems and evaluated in real-world settings. In the second half, we turn to the cross-cutting issues that surface regardless of domain, including questions of privacy, fairness, security, and governance. By weaving together technical depth and social reflection, our goal is to offer both a resource for practitioners who want to understand how AI can be effectively applied to social good and a guide for scholars and policymakers interested in the ethical and institutional frameworks needed to support responsible use.