  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.
