PrivFair: a Library for Privacy-Preserving Fairness Auditing. (arXiv:2202.04058v1 [cs.LG])

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 …

auditing fairness lg library privacy