Human Performance

How institutions govern AI and adjacent emerging technologies as systems become more autonomous, adaptive, and embedded in consequential decisions. Our work examines accountability, organizational design, deployment governance, and the institutional capabilities required when static rules are no longer enough.
Our Responsible AI work starts from a structural premise: the systems built for predictable, point-in-time AI are not enough for agentic systems and continuous-oversight regimes. The gap is not regulatory but institutional. Closing it means reorganizing model risk, compliance, technology, and legal functions into integrated governance that runs on a different clock than the traditional examination cycle.
Active research questions

• How do board risk committees set AI risk appetite when system behavior cannot be fully specified in advance?
• What does continuous lifecycle monitoring require operationally, and what is the build path for institutions starting from a compliance-only posture?
• How should the three lines of defense be restructured for agentic systems, where ownership is split across product, engineering, compliance, and legal?
• Where do MAS, EU AI Act, FDA, and SR 11-7 obligations actually converge in operational terms?
  

Active work under this pillar
Horizon Scan 001, AI Governance in Regulated Industries. The AI Governance Pulse and the Governance Readiness Assessment, both published as open-methodology tools. Research Briefs on financial-sector and healthcare AI governance. Track II working sessions on cross-jurisdictional regulatory convergence.
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