Recently, ACM FAccT published an article by Kwegyir-Aggrey and colleagues
(2023), critiquing the use of AUC ROC in predictive analytics in several
domains. In this article, we offer a critique of that article. Specifically, we
highlight technical inaccuracies in that paper's comparison of metrics,
mis-specification of the interpretation and goals of AUC ROC, the article's use
of the accuracy metric as a gold standard for comparison to AUC ROC, and the
article's application of critiques solely to AUC ROC for concerns that would
apply to the use of any metric. We conclude with a re-framing of the very valid
concerns raised in that article, and discuss how the use of AUC ROC can remain
a valid and appropriate practice in a well-informed predictive analytics
approach taking those concerns into account. We conclude by discussing the
combined use of multiple metrics, including machine learning bias metrics, and
AUC ROC's place in such an approach. Like broccoli, AUC ROC is healthy, but
also like broccoli, researchers and practitioners in our field shouldn't eat a
diet of only AUC ROC.

对于 ACM FAccT 最近发表的使用 AUC ROC 在多个领域进行预测分析的文章，我们对该文章进行了批评。具体来说，我们强调了该文章在度量标准比较、AUC ROC 的解释和目标误设、将准确率指标作为与 AUC ROC 比较的金标准以及仅将批评应用于 AUC ROC 等方面存在的技术性错误。我们通过重新定义该文章提出的问题，并讨论了在充分考虑这些问题的基础上，如何在明智的预测分析方法中继续使用 AUC ROC。我们最后讨论了多种度量标准的联合使用，包括机器学习偏差度量标准，以及 AUC ROC 在这种方法中的地位。与西兰花一样，AUC ROC 是有效的，但我们领域的研究人员和实践者不应仅仅依赖 AUC ROC。