  Despite the potential benefits of machine learning (ML) in high-risk
decision-making domains, the deployment of ML is not accessible to
practitioners, and there is a risk of discrimination. To establish trust and
acceptance of ML in such domains, democratizing ML tools and fairness
consideration are crucial. In this paper, we introduce FairPilot, an
interactive system designed to promote the responsible development of ML models
by exploring a combination of various models, different hyperparameters, and a
wide range of fairness definitions. We emphasize the challenge of selecting the
``best" ML model and demonstrate how FairPilot allows users to select a set of
evaluation criteria and then displays the Pareto frontier of models and
hyperparameters as an interactive map. FairPilot is the first system to combine
these features, offering a unique opportunity for users to responsibly choose
their model.
