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.

翻译：尽管机器学习在高风险决策领域具有潜在优势，但其部署对实践者而言仍存在门槛，且存在歧视风险。为在这些领域建立对机器学习的信任与接纳，推动机器学习工具的民主化与公平性考量至关重要。本文提出交互式系统FairPilot，通过探索不同模型、多种超参数及广泛公平性定义的组合，促进机器学习模型的负责任开发。我们聚焦于"最佳"机器学习模型选择的挑战，展示FairPilot如何允许用户选定评估标准集，并将模型与超参数的帕累托前沿以交互式地图形式呈现。作为首个整合这些功能的系统，FairPilot为用户负责任地选择模型提供了独特契机。