Across machine learning (ML) sub-disciplines, researchers make explicit
mathematical assumptions in order to facilitate proof-writing. We note that,
specifically in the area of fairness-accuracy trade-off optimization
scholarship, similar attention is not paid to the normative assumptions that
ground this approach. Such assumptions presume that 1) accuracy and fairness
are in inherent opposition to one another, 2) strict notions of mathematical
equality can adequately model fairness, 3) it is possible to measure the
accuracy and fairness of decisions independent from historical context, and 4)
collecting more data on marginalized individuals is a reasonable solution to
mitigate the effects of the trade-off. We argue that such assumptions, which
are often left implicit and unexamined, lead to inconsistent conclusions: While
the intended goal of this work may be to improve the fairness of machine
learning models, these unexamined, implicit assumptions can in fact result in
emergent unfairness. We conclude by suggesting a concrete path forward toward a
potential resolution.

该文探讨了机器学习中的公平性与准确性的权衡以及该领域中存在的数学假设，认为这些假设可能导致不一致的结论和潜在的不公平性，并提出了解决方案。