论文标题
人类的决策合作：除了学习推迟之前
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer
论文作者
论文摘要
人类AI合作（HAIC）在决策中的合作旨在在人类决策者和AI系统之间建立协同组合。学会推迟（L2D）已成为一个有前途的框架，以确定人类中的谁和人工智能应该做出哪些决定，以优化联合系统的性能和公平性。然而，L2D需要几个通常不可行的要求，例如，对于每个实例或独立于该人类的地面标签的人类预测的可用性。此外，L2D或其他方法都没有解决在现实世界中部署海克系统的基本问题，例如能力管理或处理动态环境。在本文中，我们旨在识别和审查这些局限性和其他局限性，指出HAIC未来研究的机会可能会在哪里。
Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems. Learning to defer (L2D) has been presented as a promising framework to determine who among humans and AI should make which decisions in order to optimize the performance and fairness of the combined system. Nevertheless, L2D entails several often unfeasible requirements, such as the availability of predictions from humans for every instance or ground-truth labels that are independent from said humans. Furthermore, neither L2D nor alternative approaches tackle fundamental issues of deploying HAIC systems in real-world settings, such as capacity management or dealing with dynamic environments. In this paper, we aim to identify and review these and other limitations, pointing to where opportunities for future research in HAIC may lie.
