Source-linked AI summary
Artificial Intelligence and Statistics
Bin Yu, Karl Kumbier
TL;DR
AI products and research depend on human-generated data and human input, but statistical framing is needed to connect data generation, algorithm development, and evaluation. The paper proposes the PQRS workflow, combined with randomization, local control, and stability, and illustrates these ideas in AI applications and collaborative research.
Problem
AI relies on human-machine collaboration across data generation, algorithm development, and evaluation, requiring statistical concepts to frame data-driven decisions.
Method
The paper proposes PQRS—population, question of interest, representativeness of training data, and scrutiny of results—as a framework integrating human input with statistical principles in AI.
Results
The paper discusses how PQRS, randomization, local control, and stability support interpretability, reproducibility, and data use across self-driving, medical-diagnosis, and collaborative research examples.
Takeaways & Limitations
Human expertise and statistical principles remain integral to developing, evaluating, and interpreting AI algorithms and data results.
Abstract
from arXiv · showhide
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.