The term 'algorithm bias' in data science refers to systematic favoritism exhibited by algorithms, which can lead to skewed and unfair outcomes. This bias can stem from the datasets used to train algorithms, often reflecting existing social biases or imbalances. Addressing algorithm bias is essential to ensure fairness, accuracy, and ethical use of data in decision-making processes. It plays a critical role in areas such as hiring, criminal justice, and lending, where biased results can have significant consequences on individuals and communities.