Algorithmic bias occurs when an AI system produces systematically unfair results for certain groups, usually because its training data reflects historical inequalities or lacks diversity. A recruitment model trained mostly on CVs from one city may unfairly downgrade candidates from rural regions; a credit-scoring system may disadvantage women entrepreneurs if past lending data did. Bias is rarely intentional, which makes it dangerous: companies must audit their data and test outcomes across groups before deploying AI in decisions about people.