As artificial intelligence plays an increasingly substantial role in
decisions affecting humans and society, the accountability of automated
decision systems has been receiving increasing attention from researchers and
practitioners. Fairness, which is concerned with eliminating unjust treatment
and discrimination against individuals or sensitive groups, is a critical
aspect of accountability. Yet, for evaluating fairness, there is a plethora of
fairness metrics in the literature that employ different perspectives and
assumptions that are often incompatible. This work focuses on group fairness.
Most group fairness metrics desire a parity between selected statistics
computed from confusion matrices belonging to different sensitive groups.
Generalizing this intuition, this paper proposes a new equal confusion fairness
test to check an automated decision system for fairness and a new confusion
parity error to quantify the extent of any unfairness. To further analyze the
source of potential unfairness, an appropriate post hoc analysis methodology is
also presented. The usefulness of the test, metric, and post hoc analysis is
demonstrated via a case study on the controversial case of COMPAS, an automated
decision system employed in the US to assist judges with assessing recidivism
risks. Overall, the methods and metrics provided here may assess automated
decision systems' fairness as part of a more extensive accountability
assessment, such as those based on the system accountability benchmark.

随着人工智能在影响人类和社会的决策中扮演越来越重要的角色，自动化决策系统的问责制越来越受到研究人员和从业者的关注。然而，在评估公平性方面，文献中存在着大量采用不同视角和假设的公平度量标准，这通常是不兼容的。本文提出了一个新的等混乱公平测试来检查自动化决策系统的公平性，并提出了一个新的混淆平等误差来量化任何不公平性的程度。通过对自动化决策系统 COMPAS 进行案例研究，证明了这里提供的测试、度量和事后分析的实用性。