Holistic Survey of Privacy and Fairness in Machine Learning
Sina Shaham Arash Hajisafi Minh K Quan Dinh C Nguyen Bhaskar Krishnamachari Charith Peris Gabriel Ghinita Cyrus Shahabi Pubudu N. Pathirana
Privacy and fairness are two crucial pillars of responsible Artificial
Intelligence (AI) and trustworthy Machine Learning (ML). Each objective has
been independently studied in the literature with the aim of reducing utility
loss in achieving them. Despite the significant interest attracted from both
academia and industry, there remains an immediate demand for more in-depth
research to unravel how these two objectives can be simultaneously integrated
into ML models. As opposed to well-accepted trade-offs, i.e., privacy-utility
and fairness-utility, the interrelation between privacy and fairness is not
well-understood. While some works suggest a trade-off between the two objective
functions, there are others that demonstrate the alignment of these functions
in certain scenarios. To fill this research gap, we provide a thorough review
of privacy and fairness in ML, including supervised, unsupervised,
semi-supervised, and reinforcement learning. After examining and consolidating
the literature on both objectives, we present a holistic survey on the impact
of privacy on fairness, the impact of fairness on privacy, existing
architectures, their interaction in application domains, and algorithms that
aim to achieve both objectives while minimizing the utility sacrificed.
Finally, we identify research challenges in achieving privacy and fairness
concurrently in ML, particularly focusing on large language models.