"Algorithm bias" in machine learning occurs when an algorithm produces systematic errors due to prejudicial assumptions or data that reflect existing stereotypes or inequities. This can result in unfair treatment or inaccurate predictions for certain demographics. Bias can arise from training data that is not representative or from flaws in the algorithm's design. Addressing algorithm bias is essential for ensuring ethical AI development, which promotes fairness and reduces discrimination in automated decisions.