Foundations of Data Science
Algorithmic bias refers to the systematic and unfair discrimination that can occur in algorithms, leading to outcomes that disadvantage certain individuals or groups based on characteristics such as race, gender, or socioeconomic status. This phenomenon arises when the data used to train algorithms reflects existing prejudices or inequalities, affecting decision-making processes in areas like hiring, criminal justice, and lending. Understanding algorithmic bias is crucial for ensuring fairness and accountability in machine learning applications and the overall data science lifecycle.
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