Statistical parity metrics have been widely studied and endorsed in the AI
community as a means of achieving fairness, but they suffer from at least two
weaknesses. They disregard the actual welfare consequences of decisions and may
therefore fail to achieve the kind of fairness that is desired for
disadvantaged groups. In addition, they are often incompatible with each other,
and there is no convincing justification for selecting one rather than another.
This paper explores whether a broader conception of social justice, based on
optimizing a social welfare function (SWF), can be useful for assessing various
definitions of parity. We focus on the well-known alpha fairness SWF, which has
been defended by axiomatic and bargaining arguments over a period of 70 years.
We analyze the optimal solution and show that it can justify demographic parity
or equalized odds under certain conditions, but frequently requires a departure
from these types of parity. In addition, we find that predictive rate parity is
of limited usefulness. These results suggest that optimization theory can shed
light on the intensely discussed question of how to achieve group fairness in
AI.

该论文通过优化社会福利功能（SWF）来评估各种公平性定义，探讨了广义社会公正观念是否对实现 AI 中的公平性有用，结果表明最优解可以在特定条件下证明人口平等或平等几率，但通常需要偏离这些平等类型。此外，预测率平衡的实用性有限，这些结果表明优化理论可以为如何在 AI 中实现群体公平性的讨论问题提供启示。