FairAutoML: Embracing Unfairness Mitigation in AutoML

Understand

In this work, we propose an Automated Machine Learning (AutoML) system to search for models not only with good prediction accuracy but also fair.

• We first investigate the necessity and impact of unfairness mitigation in the AutoML context.
• We establish the FairAutoML framework.
• The framework provides a novel design based on pragmatic abstractions, which makes it convenient to incorporate existing fairness definitions, unfairness mitigation techniques, and hyperparameter search methods into the model search and evaluation process.