Bias Mitigation
Bias mitigation refers to the set of techniques and practices used to identify, measure, and reduce unfair biases in data, algorithms, and machine learning models. It aims to ensure that automated systems do not perpetuate or amplify existing societal prejudices, such as those based on race, gender, age, or other protected characteristics. This involves processes like data preprocessing, algorithmic adjustments, and post-hoc evaluations to promote fairness and equity in AI-driven decisions.
Developers should learn bias mitigation to build ethical and compliant AI systems, especially in high-stakes domains like hiring, lending, healthcare, and criminal justice where biased outcomes can cause real-world harm. It is crucial for meeting regulatory requirements (e.g., GDPR, AI Act), enhancing model robustness, and fostering trust with users by ensuring predictions are fair across diverse groups. Without it, models risk reinforcing discrimination and facing legal or reputational consequences.