Explore how bias enters AI systems and why fairness in algorithms is crucial for ethical decision-making in business and society. This article explores common sources of algorithmic bias—from skewed training data to design assumptions—and explains the fairness standards that organizations must adopt to prevent discrimination and build trust. Readers will learn practical strategies to identify bias, evaluate fairness metrics, and implement governance measures for the responsible deployment of algorithms. Whether you’re a tech leader, data scientist, or policy maker, this guide equips you with the insights needed to develop AI that works for everyone.