Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and research. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors' collaborative research.

翻译：人工智能(AI)本质上是数据驱动的,它要求通过在数据生成、算法开发以及结果评估过程中的人力机器合作应用统计概念,本文讨论了如何通过人口统计概念、兴趣问题、培训数据的代表性和结果审查(PQRS)来开展这种人力机器合作,《减贫战略》工作流程为将统计思想与人类投入纳入AI产品和研究提供了一个概念框架,其中包括随机化和地方控制的试验性设计原则以及稳定原则,以获得算法和数据结果的可复制性和可解释性,我们讨论了在自行驾驶汽车、自动医学诊断和作者合作研究实例方面使用这些原则的情况。