Algorithmic bias plays a significant role in artificial intelligence by affecting the outcomes of algorithms trained on biased data. This bias can lead to unfair and discriminatory practices, impacting areas such as hiring, law enforcement, and lending. For instance, if historical data used to train an AI system reflects societal inequalities, the system may perpetuate these biases in its predictions. Addressing algorithmic bias is crucial for ethical AI deployment, requiring diverse data, regular audits, and transparency in AI processes. Ultimately, reducing bias enhances trust in AI technologies and ensures fair treatment for all individuals.