Abstract

As artificial intelligence (AI) becomes integral to business decision-making, ensuring fairness, equity, and diversity in automated systems is a critical ethical and operational priority. This chapter explores the concept of fairness in AI, examining various types of bias, their sources, and real-world consequences. It reviews legal and ethical frameworks, outlines technical and organizational strategies for promoting inclusive AI, and presents case studies highlighting both best practices and failures. Emphasis is placed on the importance of human oversight, diverse representation, and transparency in AI development. The chapter concludes by identifying challenges and proposing future research directions to align AI with broader diversity and inclusion goals.