Understanding Fairness Metrics
Fairness metrics are crucial in assessing and improving the fairness of machine learning models. They help identify if a model is biased against certain groups of people. Here’s a brief overview of some common fairness metrics:
- Disparate Impact: Measures the difference in outcomes between different groups. A high disparate impact suggests that the model might be biased against one group.
- Equalized Odds: Ensures that the false positive rate for each group is equal. This metric is often used in binary classification tasks.
- Demographic Parity: Measures the difference in true positive rates between different groups. It aims to ensure that each group has an equal chance of being correctly classified as positive.
Fairness Metrics Diagram
For a more detailed explanation, you can visit our Introduction to Fairness Metrics.
Fairness in machine learning is an ongoing challenge. As models become more complex, it’s essential to continuously evaluate and improve their fairness. By using the right metrics, we can ensure that our models are making decisions that are fair and unbiased.