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.