一句话：提出 MANILA 低代码 Web 应用，基于扩展特征模型引导用户基准测试机器学习模型与公平性方法并选择最佳权衡。
针对机器学习模型公平性增强方法繁多、缺少统一易用基准测试与选型工具的问题，本文提出 MANILA——一个基于 Web 的低代码应用。MANILA 以扩展特征模型（Extended Feature Model）将一般公平性基准测试工作流建模为软件产品线，利用特征间约束引导用户构建不会导致执行错误的实验，并对多种机器学习模型与公平性增强方法进行基准测试，从中选择公平性与有效性权衡最佳的方案。作者描述了 MANILA 的架构与实现，并从表达性和正确性两方面进行评估。结果表明该工具有效支持低代码方式下的公平性基准测试，并通过特征约束保证实验可执行性。
This paper presents MANILA, a web-based low-code application to benchmark machine learning models and fairness-enhancing methods and select the one achieving the best fairness and effectiveness trade-off. It is grounded on an Extended Feature Model that models a general fairness benchmarking workflow as a Software Product Line. The constraints defined among the features guide users in creating experiments that do not lead to execution errors. We describe the architecture and implementation of MANILA and evaluate it in terms of expressiveness and correctness.