AI systems have moved from experimentation to operational dependency. Algorithms now influence hiring, credit, scheduling, underwriting, forecasting, and clinical workflows. As reliance deepens, so does exposure. Regulatory scrutiny is intensifying, legal standards are shifting, and boards are asking harder questions. The risk is no longer just model accuracy — it is accountability, bias, explainability, vendor dependency, and reputational consequence. Organizations that treat governance as a compliance afterthought are accumulating structural risk. Those that build clear oversight, ownership, and escalation pathways are managing liability without sacrificing innovation speed.
This Session Will Examine:
- Defining Accountability in AI-Supported Decisions: Establishing who owns outcomes when algorithmic systems influence or initiate consequential decisions.
- Balancing Innovation with Risk Control: Building governance structures that enable responsible experimentation without creating unmanaged liability exposure.
- Vendor and Model Oversight Discipline: Applying rigorous standards for managing third-party AI tools, data access, and model transparency.
- Regulatory and Legal Preparedness: Assessing how evolving requirements around bias, explainability, and documentation are reshaping enterprise risk frameworks.
- Board-Level Reporting and Scenario Planning: Examining how executive teams are translating AI risk exposure into board-level language and actionable scenario planning.