Frontiers of Information Technology & Electronic Engineering· 2018Q2
Artificial intelligence and statistics
- 1,995citations
- Q2SCImago
- 2018year
Short summary
A new 'Population, Question of Interest, Representativeness, Scrutiny' (PQRS) workflow is proposed to integrate statistical concepts and human collaboration into AI development.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces the PQRS workflow for human-machine collaboration in AI.
- Integrates statistical concepts like population, representativeness, and scrutiny into AI.
- Applies principles of randomization, local control, and stability for reproducibility.
- Demonstrates application in self-driving cars and automated medical diagnoses.
AI-generated from the title and abstract; the full text is not read.
Abstract
Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the 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 researches. 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.
The authors' abstract, as published at the source. Frontiers of Information Technology & Electronic Engineering, 2018 · DOI ↗
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