Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to computes and verify the fairness of any machine learning (ML) model. In the deisgn of FaaS, the data and outcomes are represented through cryptograms to ensure privacy. Also, zero knowledge proofs guarantee the well-formedness of the cryptograms and underlying data. FaaS is model--agnostic and can support various fairness metrics; hence, it can be used as a service to audit the fairness of any ML model. Our solution requires no trusted third party or private channels for the computation of the fairness metric. The security guarantees and commitments are implemented in a way that every step is securely transparent and verifiable from the start to the end of the process. The cryptograms of all input data are publicly available for everyone, e.g., auditors, social activists and experts, to verify the correctness of the process. We implemented FaaS to investigate performance and demonstrate the successful use of FaaS for a publicly available data set with thousands of entries.

翻译：公平机器学习是一个蓬勃发展的研究课题。本文提出“公平性即服务”（FaaS）——一种安全、可验证且隐私保护的协议，用于计算和验证任意机器学习模型的公平性。在FaaS设计中，数据和结果通过密码表示以保障隐私，同时零知识证明确保密码及底层数据的正确性。FaaS是模型无关的，可支持多种公平性度量指标，因此能作为审计任意ML模型公平性的服务。我们的方案无需可信第三方或私有通道来计算公平性度量指标。安全保证与承诺的实现方式确保从过程开始到结束的每一步都安全透明且可验证。所有输入数据的密码均公开可用，供审计员、社会活动家及专家等任何人验证过程的正确性。我们实现了FaaS以探究其性能，并成功演示了其在包含数千条记录的公开数据集上的应用。