Overview:
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 becomes 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)
Course Objective:
Teach participants and how to implement data governance, inventory and contract provisions that help ensure high quality data is available.
By the end of this session, participants will:
- Understand the legal and operational importance of data governance in AI systems.
- Learn how to develop and maintain a data inventory to track and assess datasets used in AI.
- Gain practical insight into using audit mechanisms and contractual provisions to ensure accountability, data quality, and legal compliance.
- Be able to advise clients or organizations on how to implement a data stewardship framework that aligns with both legal and ethical standards in AI deployment.
Target Audience:
- Attorneys, compliance officers and anyone managing AI projects in organizations.
- In-House Counsel and Legal Advisors supporting organizations that develop, procure, or use AI systems
- Privacy and Data Protection Lawyers looking to expand their knowledge into AI-specific data governance
- Compliance Officers and Risk Managers focused on regulatory and contractual controls around data use in AI
- Technology and IP Lawyers advising clients on AI implementation, licensing, and data rights
- Law Firm Associates and Partners interested in emerging legal frameworks in AI and data management
- Legal Operations Professionals involved in data inventory, audits, and governance processes
- General Counsels who need to understand organizational risk related to data quality and AI accountability
Basic Knowledge:
- None, No deep technical expertise required, but a willingness to engage with interdisciplinary concepts involving law, technology, and data governance is expected
- Basic understanding of legal and regulatory concepts, especially in data protection, privacy (e.g., GDPR, CCPA), and contract law
- Familiarity with AI or data-driven technologies (at least conceptually), including how data is used in training and deploying AI systems
- General awareness of organizational data practices, such as data collection, sharing, storage, and compliance requirements