Abstract
Machine learning applications in high-stakes scenarios should always operate under human oversight. Developing an optimal combination of human and machine intelligence requires an understanding of their complementarities, particularly regarding the similarities and differences in the way they make mistakes. We perform extensive experiments in the area of face recognition and compare two automated face recognition systems against human annotators through a demographically balanced user study. Our research uncovers important ways in which machine learning errors and human errors differ from each other, and suggests potential strategies in which human-machine collaboration can improve accuracy in face recognition.
Abstract (translated)
在高风险场景中应用机器学习时，系统应始终处于人类监督之下。为了开发出最佳的人机智能组合，需要理解它们各自的互补性，特别是关于他们在犯错方式上的相似性和差异性。我们通过一项人口学平衡的用户研究，在面部识别领域进行了广泛的实验，并将两个自动化的面部识别系统与人工标注员进行比较。我们的研究表明机器学习错误和人类错误之间存在重要的不同之处，并提出了可能的人机协作策略，以提高面部识别的准确性。
URL
https://arxiv.org/abs/2502.11337
