Data Quality Control in AI: Navigating the AI Data Stewardship Framework

Data is the lifeblood of generative AI applications. While these applications depend on access to enormous amounts of data, we see now that this is not enough. At this point, the relationship between quality and quantity is clear: data quality and quantity are equally important; scarcity in one inevitably destabilizes the other. The dynamics of this relationship become most evident by the performance of these applications - they are ultimately only as good as the data they train on. 

The AI Data Stewardship Framework (AI-DSF) is a framework designed to ensure high-quality data is continuously provided. This course provides a generous overview that surveys the AI-DSF, including its purpose and selected use cases, and explains how the framework's various controls collaborate to ensure the provision of high-quality data.

Learning Objectives:

1. Explore the AI Data Stewardship Framework (AI-DSF) and its role in ensuring high-quality data provision
2. Review selected use cases demonstrating the effectiveness of the AI-DSF
3. Gain insights into the various controls within the AI-DSF and how they collaborate to maintain data quality
4. Identify best practices for implementing the AI-DSF in AI projects to improve performance and reliability