Verifiable Fairness: Privacy-preserving Computation of Fairness for
  Machine Learning Systems
Ehsan Toreini Maryam Mehrnezhad Aad van Moorsel
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.