The Role Of Data And Its Quality In Supporting AI Systems

By Mohammad Hossein Jarrahi et al

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Table of Contents

1. Introduction
2. Data-Centric AI (DCAI)
3. Principles of DCAI
4. Systematic Improvement of Data Fit
5. Systematic Improvement of Data Consistency
6. Mutual Improvement of Model and Data through Iteration

Summary

The document discusses the growing importance of data quality in AI systems and introduces the concept of Data-Centric AI (DCAI). DCAI emphasizes the continuous improvement of data quality and dynamism in AI systems. It presents six guiding principles of DCAI, focusing on systematic improvement of data fit and consistency, as well as mutual enhancement of model and data through iterative processes. The authors highlight the human-centered nature of data in feeding AI systems, advocating for a more comprehensive approach that integrates data and model enhancements throughout the AI life cycle.