Algorithmic Bias Mitigation

Algorithmic bias mitigation refers to the strategies and techniques employed to reduce or eliminate biases present in AI systems and algorithms. These biases can arise from various sources, including biased training data, flawed algorithms, or societal stereotypes. Main characteristics of bias mitigation include identifying bias sources, implementing corrective measures, and continuously monitoring outcomes to ensure fairness. Common use cases involve improving the fairness of machine learning models in areas such as hiring, lending, and law enforcement, where biased outcomes can have significant societal impacts.