Artificial Intelligence and Statistics

TL;DR

This paper explores how statistical concepts like PQRS and experimental design principles can enhance human-machine collaboration in AI development, focusing on data representativeness, reproducibility, and interpretability.

Contribution

It introduces the PQRS framework integrating statistical ideas with human input to improve AI research and applications.

Findings

PQRS provides a conceptual framework for AI development.

Applying experimental design principles enhances AI reproducibility.

Case studies include self-driving cars and automated medical diagnoses.

Abstract

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.