Equalized Odds Postprocessing

Postprocessing Fairness algorithm

Inputs

• Data: reference dataset
• Preprocessor: preprocessing methods
• Learner: learner to be postprocessed

Outputs

• Learner: learner with Fairness postprocessing
• Model: a trained model with Fairness postprocessing

Equalized Odds postprocessing Postprocessing fairness algorithm which modifies the predictions of any given classifier to meet certain fairness criteria. It works by first fitting the learner to the training data, creating a model, and using it to get the predictions. It uses these predictions to fit the Equalized Odds Postprocessing algorithm, which creates a post-processor. This post-processor is then used to adjust the model’s predictions on the test data.

Example

In this example we will use the Equalized Odds Postprocessing to debias the predictions of a linear regression model. All we need to do is include the Equalized Odds Postprocessing widget in our workflow and connect any model and desired preprocessors to it. We then connect the Equalized Odds Postprocessing to the Test & Score widget along with the dataset to evaluate the performance of the model with postprocessing.