Measuring bias
To successfully combat bias, we must first measure its existence and understand its impact on our ML models. Several statistical methods and techniques have been developed for this purpose, each offering a different perspective on bias and fairness. Here are a few essential methods:
- Confusion matrix: A fundamental tool for evaluating the performance of an ML model, the confusion matrix can also reveal bias. It allows us to measure false positive and false negative rates, which can help us identify situations where the model performs differently for different groups.
- Disparate impact analysis: This technique measures the ratio of favorable outcomes for a protected group compared to a non-protected group. If the ratio is significantly below one, it implies a disparate impact on the protected group, signaling potential bias.
- Equality of odds: This method requires that a model’s error rates be equal across different groups. In other words, if a model...
