AI Fairness for Responsible Intelligence

Fairness in artificial intelligence is no longer an abstract concern-it shapes decisions in healthcare, education, housing, and beyond. AI Fairness for Responsible Intelligence treats fairness not as an isolated algorithmic objective, but as the organizing principle of responsible intelligence, integrating technical foundations with privacy, security, governance, and societal impact throughout the AI lifecycle.

Beginning with the foundations of responsible AI and machine learning, the book examines formal fairness definitions, sources of bias in data and algorithms, and algorithmic approaches for mitigating unfairness. It then explores fairness in natural language processing, computer vision, graph learning, and foundation models, while addressing researcher bias, scientific rigor, and real-world case studies that highlight current challenges and emerging research directions.

Written for advanced undergraduate and graduate students, researchers, instructors, and practitioners across AI, machine learning, data science, healthcare, public policy, and related disciplines, the book equips readers to critically evaluate, design, deploy, and govern fair and trustworthy AI in real-world settings.