Legal Responsibility for AI Agents: Managing Risk Through Compliance-by-Design (On-Demand)

Course Description

AI officially started with the 1956 Dartmouth summer conference. But it didn’t flourish until the internet data renaissance allowed AI to blossom as we know it today. After a quick overview of the AI timeline, we turn to the concepts of AI and Agentic AI. From there we will explore AI bias, and the potential harms from AI and Agentic AI. We will discuss how to nurture innovation while managing risk to win the AI race.

Principles

• Effective AI Governance Begins with Risk Identification
  • Organizations must identify legal, operational, and ethical risks associated with AI and agentic AI before they can implement appropriate governance, compliance, and mitigation strategies.
• Responsibility for AI Systems Requires Clear Accountability
  • Developers, deployers, vendors, and users each play a role in managing AI risks. Clearly allocating responsibilities through governance structures, policies, and contracts is essential to reducing legal exposure.
• Data Governance Is Fundamental to AI Compliance
  • The quality, provenance, and authorized use of data directly affect AI reliability and legal compliance. Lawful access, proper authorization, and strong data governance are critical throughout the AI lifecycle.
• Compliance-by-Design Integrates AI Risk Management into Existing Frameworks
  • AI governance is most effective when embedded into established privacy, cybersecurity, and enterprise risk management programs through proactive policies, contractual safeguards, and recognized AI risk management frameworks.

Syllabus

1. Manage risk by first identifying the risk
2. Who is responsible for AI and AI agents?
3. Data provenance: Access does not always mean authorization
4. How to use policies and contract language to identify and minimize risks, obligations, and responsibilities
5. How AI risk management frameworks fit into existing privacy and cybersecurity risk management frameworks