  As the frontier of machine learning applications moves further into human
interaction, multiple concerns arise regarding automated decision-making. Two
of the most critical issues are fairness and data privacy. On the one hand, one
must guarantee that automated decisions are not biased against certain groups,
especially those unprotected or marginalized. On the other hand, one must
ensure that the use of personal information fully abides by privacy regulations
and that user identities are kept safe. The balance between privacy, fairness,
and predictive performance is complex. However, despite their potential
societal impact, we still demonstrate a poor understanding of the dynamics
between these optimization vectors. In this paper, we study this three-way
tension and how the optimization of each vector impacts others, aiming to
inform the future development of safe applications. In light of claims that
predictive performance and fairness can be jointly optimized, we find this is
only possible at the expense of data privacy. Overall, experimental results
show that one of the vectors will be penalized regardless of which of the three
we optimize. Nonetheless, we find promising avenues for future work in joint
optimization solutions, where smaller trade-offs are observed between the three
vectors.
