Fairness-Driven Private Collaborative Machine Learning
This addresses fairness issues for parties in domains like medicine and finance using collaborative ML, but it is incremental as it builds on existing private collaborative methods.
The paper tackles the problem of fairness in private collaborative machine learning, where data sharing among parties improves performance but raises privacy and fairness concerns, and shows that their proposed method enhances fairness significantly with only a minor accuracy compromise.
The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated methods for private collaborative machine learning, the fairness of such collaborative algorithms was overlooked. In this work we suggest a feasible privacy-preserving pre-process mechanism for enhancing fairness of collaborative machine learning algorithms. Our experimentation with the proposed method shows that it is able to enhance fairness considerably with only a minor compromise in accuracy.