对比人脸识别中人与机器的错误模式，探索协同提升准确率的方法。
A Comparison of Human and Machine Learning Errors in Face Recognition
- 通过平衡人口统计学的用户研究，比较机器与人类在人脸识别中的错误。
- 发现机器与人类的错误具有不同特征，可互补以减少整体误判。
- 适合关注人机协作、安全认证系统优化的研究者参考。
在高风险场景中，机器学习应用应始终有人类监督。要实现人机智能的最佳结合，需理解两者在错误模式上的互补性，特别是其相似与差异。本研究在人脸识别领域开展广泛实验，通过一个体现人口统计学平衡的用户研究，将两种自动人脸识别系统与人工标注者进行对比。研究揭示了机器学习错误与人类错误之间的重要差异，并提出了潜在的人机协作策略，以提高人脸识别的整体准确性。
原文摘要 · Abstract (English)
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.
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