The implications of machine learning bias are profound, as biased algorithms can lead to unfair treatment and perpetuate existing inequalities. When machine learning models are trained on biased data, they can produce skewed outcomes that disproportionately affect certain groups. This can impact areas such as hiring, law enforcement, and healthcare, leading to systemic discrimination. Recognizing and mitigating bias is crucial for developing ethical AI systems and ensuring equitable access to technology. Addressing these biases involves diversifying training data and implementing comprehensive evaluation frameworks to promote fairness.