Artificial Intelligence and Statistics
It addresses the need for more robust and interpretable AI systems across domains like self-driving cars and medical diagnosis, but it is incremental as it builds on existing statistical concepts without introducing new methods.
The paper tackles the challenge of integrating statistical principles into AI development through a conceptual framework called PQRS, which emphasizes human-machine collaboration across data generation, algorithm development, and result evaluation to enhance reproducibility and interpretability.
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.