Fairness Metrics

Fairness metrics are quantitative measures used to evaluate and mitigate bias in machine learning models, particularly in areas like hiring, lending, and criminal justice. They assess whether a model's predictions are equitable across different demographic groups (e.g., based on race, gender, or age) by analyzing statistical disparities in outcomes. These metrics help ensure that AI systems do not perpetuate or amplify existing societal inequalities.

Also known as: AI fairness metrics, Bias metrics, Equity metrics, Fairness measures, ML fairness

Why learn Fairness Metrics?

Developers should learn and use fairness metrics when building or deploying machine learning models in high-stakes domains where biased predictions could cause harm, such as in finance, healthcare, or employment. They are essential for regulatory compliance (e.g., with laws like the EU AI Act), ethical AI development, and building trust with users by promoting transparency and accountability in automated decision-making systems.

Learning Resources

• book: Fairness and Machine Learning
• docs: AI Fairness 360 Toolkit Documentation
• course: Fairness in Machine Learning - Coursera Course
• tutorial: Measuring Fairness in Machine Learning - Towards Data Science Article
• video: Fairness Metrics Explained - YouTube Video