This session, “AI Data Stewardship Framework – How Lawyers Can Use This Critical Tool,” equips legal professionals with foundational knowledge and practical strategies to manage data effectively in the context of AI. As AI systems increasingly rely on large, complex datasets, ensuring data quality, compliance, and traceability becomes essential. Lawyers play a key role in implementing and advising on frameworks that support responsible AI use through robust data governance, inventory practices, and contractual controls.

Explains how the framework’s controls work to help ensure high-quality data is consistently supplied and made available. Data is the lifeblood of generative AI applications. And while these applications depend on access to enormous amounts of it, that is not enough. At this point, we can see the relationship between quality and quantity: 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. There are various ways of defining the optimal data set, one that exhibits sufficient quality and quantity. Here I call it simply “high quality” data. It is obvious, therefore, that having and maintaining policies and procedures that are specifically designed to ensure high-quality data is continuously provided is critical. I refer to this overall effort as the AI Data Stewardship Framework (AI-DSF).