论文标题
学会补充人类
Learning to Complement Humans
论文作者
论文摘要
在开放世界中，对人工智能的愿景不断提高，集中在可以与人类相辅相成的感知，诊断和推理任务的系统的发展。迄今为止，旨在补充人们的技能的系统已经采用了经过培训的模型，以孤立地进行了尽可能准确的训练。我们演示了如何通过考虑人和机器的独特能力来优化人机团队的综合性能，以优化端到端的学习策略。目的是将机器学习重点放在对人类困难的问题实例上，同时认识到机器困难并寻求人类投入的实例。我们在两个现实世界中的域（科学发现和医学诊断）中证明了通过这些方法构建的人机团队的表现优于机器和人员的个体性能。然后，我们分析了这种互补性最强的条件，哪种训练方法会放大它。综上所述，我们的工作提供了第一个系统的调查，以了解如何培训机器学习系统以补充人类推理。
A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks. To date, systems aimed at complementing the skills of people have employed models trained to be as accurate as possible in isolation. We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams by considering the distinct abilities of people and machines. The goal is to focus machine learning on problem instances that are difficult for humans, while recognizing instances that are difficult for the machine and seeking human input on them. We demonstrate in two real-world domains (scientific discovery and medical diagnosis) that human-machine teams built via these methods outperform the individual performance of machines and people. We then analyze conditions under which this complementarity is strongest, and which training methods amplify it. Taken together, our work provides the first systematic investigation of how machine learning systems can be trained to complement human reasoning.
