  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.
Following this framework, we develop a fair AutoML system based on an existing
AutoML system. The augmented system includes a resource allocation strategy to
dynamically decide when and on which models to conduct unfairness mitigation
according to the prediction accuracy, fairness, and resource consumption on the
fly. Extensive empirical evaluation shows that our system can achieve a good
`fair accuracy' and high resource efficiency.
