PrivFair: a Library for Privacy-Preserving Fairness Auditing
This addresses the challenge of privacy-preserving fairness auditing for stakeholders like companies and investigators, though it is incremental as it builds on existing MPC techniques.
The authors tackled the problem of auditing machine learning models for fairness without compromising privacy by introducing PrivFair, a library that uses Secure Multiparty Computation to protect both the model and sensitive audit data, enabling external audits without disclosing unencrypted information.
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance. ML models have been found to exhibit discrimination based on sensitive attributes such as gender, race, or disability. Assessing if an ML model is free of bias remains challenging to date, and by definition has to be done with sensitive user characteristics that are subject of anti-discrimination and data protection law. Existing libraries for fairness auditing of ML models offer no mechanism to protect the privacy of the audit data. We present PrivFair, a library for privacy-preserving fairness audits of ML models. Through the use of Secure Multiparty Computation (MPC), PrivFair protects the confidentiality of the model under audit and the sensitive data used for the audit, hence it supports scenarios in which a proprietary classifier owned by a company is audited using sensitive audit data from an external investigator. We demonstrate the use of PrivFair for group fairness auditing with tabular data or image data, without requiring the investigator to disclose their data to anyone in an unencrypted manner, or the model owner to reveal their model parameters to anyone in plaintext.