Equal Odds Difference
Overview
Equalized Odds is a fairness metric used to assess whether a classification model provides equal predictive performance across different groups or populations. It focuses on measuring the balance of true positive rates (sensitivity/recall) and true negative rates (specificity) between the groups.
Calculation
Equalized Odds = Privileged Group True Positive Rate - Unprivileged Group True Positive Rate + Privileged Group True Negative Rate - Unprivileged Group True Negative Rate
Where:
- True Positive Rate = True Positives / (True Positives + False Negatives)
- False Negative Rate = False Negatives / (True Positives + False Negatives)
Usage
Manually
# Calculate equal odds difference
result = equal_odds_difference(df, protected_attribute, privileged_group, labels, positive_label, y_true)
print("Equal Odds Difference:", result)
Using Fairness Object
result = (fo.compute(equal_odds_difference))
Results
Equal Odds Difference: 0.33333333331666665
These results are obtained by using the input data given in the Create Example Data page under Getting Started
Interpretation
The Equalized Odds metric quantifies the difference in true positive rates (sensitivity/recall) and true negative rates (specificity) between two groups or populations. A positive value indicates that the model performs better in terms of true positive rates and true negative rates for the first group compared to the second group, suggesting potential disparities in predictive performance.
Updated 10 months ago