Algorithm bias in AI development can lead to inequitable outcomes, causing systems to favor certain groups over others. This bias often arises from skewed training data, which does not accurately represent the diversity of the population. As AI systems are increasingly used in critical areas like hiring, law enforcement, and healthcare, biased algorithms may reinforce existing discrimination and worsen social inequalities. Addressing algorithm bias is crucial to ensure fairness and build trust in AI technologies, necessitating rigorous data audits and diverse training datasets.