Abstract
Human review of consequential decisions by face recognition algorithms creates a "collaborative" human-machine system. Individual differences between people and machines, however, affect whether collaboration improves or degrades accuracy in any given case. We establish the circumstances under which combining human and machine face identification decisions improves accuracy. Using data from expert and non-expert face identifiers, we examined the benefits of human-human and human-machine collaborations. The benefits of collaboration increased as the difference in baseline accuracy between collaborators decreased-following the Proximal Accuracy Rule (PAR). This rule predicted collaborative (fusion) benefit across a wide range of baseline abilities, from people with no training to those with extensive training. Using the PAR, we established a critical fusion zone, where humans are less accurate than the machine, but fusing the two improves system accuracy. This zone was surprisingly large. We implemented "intelligent human-machine fusion" by selecting people with the potential to increase the accuracy of a high-performing machine. Intelligent fusion was more accurate than the machine operating alone and more accurate than combining all human and machine judgments. The highest system-wide accuracy achievable with human-only partnerships was found by graph theory. This fully human system approximated the average performance achieved by intelligent human-machine collaboration. However, intelligent human-machine collaboration more effectively minimized the impact of low-performing humans on system-wide accuracy. The results demonstrate a meaningful role for both humans and machines in assuring accurate face identification. This study offers an evidence-based road map for the intelligent use of AI in face identification.
Abstract (translated)
人类审查人脸识别算法做出的重要决定，创造了一个“协作”的人机系统。然而，人们与机器之间的个体差异会影响合作是否在特定情况下提高或降低准确性。我们确定了结合人和机器面部识别决策以提高准确性的条件。通过使用专家和非专家的面部标识数据，我们研究了人类之间以及人机协作的好处。当合作者的基础准确度之差减小时，协作的优势会增加——遵循接近准确规则（PAR）。这条规则预测了在广泛的基准能力范围内，从未经训练的人到接受过广泛培训的人的合作（融合）收益。 使用PAR，我们在一个关键的融合区域建立了人类机器合作模型，在这个区域内，人类比机器更不准确，但将两者结合可以提高系统的准确性。这一区域出乎意料地大。通过选择有潜力提升高性能机器精度的人类参与者，“智能人机融合”得以实施。这种智能融合不仅比单独运作的机器更精确，而且也比组合所有的人工和机器判断更有效。 完全由人类组成的系统达到了最高的系统级准确率，这是通过图论找到的。这个全人类的系统大致等同于智能人机合作所实现的平均性能。然而，智能人机协作在最小化低效人工对整体系统准确性的影响方面更为高效。 这些结果表明了人类和机器都在确保面部识别准确性的过程中发挥着重要作用。这项研究提供了一个基于证据的方法来指导人工智能在面部识别中的智能化应用。
URL
https://arxiv.org/abs/2510.02570
