The implications of algorithmic bias in AI systems can lead to unfair treatment of individuals based on race, gender, or socioeconomic status. Bias can arise from skewed training data or flawed model design, resulting in discriminatory outcomes. This presents ethical challenges and may perpetuate societal inequalities. Additionally, biased AI can cause reputational damage to organizations and lead to regulatory scrutiny. Addressing AI bias requires implementing fairness metrics, diverse data sets, and rigorous evaluation protocols to ensure equitable technology deployment.