Associate Vice President, Model & AI Governance

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The Associate Vice President, Model & AI Governance, is the enterprise leader responsible for establishing and overseeing the organization’s framework for model governance, artificial intelligence (AI) risk management, responsible AI and AI governance.
Reporting to the Chief Audit & Risk Officer, this executive provides independent second-line oversight and challenge of the organization’s use of models, advanced analytics, machine learning, generative AI, and emerging AI technologies. The role establishes the governance, risk-management, control, monitoring and escalation framework necessary to ensure AI models are deployed in a manner that is safe, ethical, transparent, explainable, compliant, secure and aligned with the organization’s mission and risk appetite.
The Associate Vice President, Model & AI Governance, partners closely with executive leadership, technology, data and analytics, clinical/business leaders, compliance, legal, privacy, cybersecurity, information security, internal audit, enterprise risk management and other control functions to ensure AI-related risks are identified, assessed, governed, monitored, and appropriately reported.
This leader will serve as an advisor to executive management on emerging model and AI risks, while maintaining appropriate independence from the teams developing and deploying models and AI solutions.

Key Responsibilities
- Own and continuously enhance the enterprise model governance framework, including model identification, inventory, classification, risk tiering, development, validation, approval, implementation, monitoring, change management, retirement and documentation.
- Define model risk appetite, risk taxonomy, minimum control standards, governance requirements and escalation thresholds.
- Provide effective challenge over model development, validation, implementation, monitoring and performance.
- Oversee the enterprise model inventory and ensure appropriate ownership, accountability, documentation, and risk classification.
- Establish standards for model validation, including conceptual soundness, performance, stability, data quality, bias, explainability and ongoing monitoring.
- Ensure appropriate governance over third-party and externally developed models.
- Develop and oversee the organization’s Responsible AI framework and principles.
- Establish governance standards addressing fairness, bias, transparency, explainability, accountability, privacy, human oversight, safety, security, and appropriate use of AI.
- Establish processes for AI use-case risk intake, risk assessment, approval, monitoring, and ongoing oversight, working closely with other AI governance functions to simplify and streamline processes.
- Develop standards for responsible use of generative AI, large language models, AI agents, automated decision-making and other emerging technologies, partnering with other stakeholders to ensure cross functional alignment
- Establish governance mechanisms to identify and address unintended consequences, model drift, hallucination, discrimination, explainability limitations, and other AI-specific risks.
- Own the enterprise AI risk management framework and associated governance processes.
- Establish governance requirements and oversight for the enterprise AI inventory, ensuring AI applications and use cases are appropriately inventoried, classified, risk-rated, assigned accountable ownership, and monitored throughout their lifecycle.
- Develop AI risk assessments and approval requirements proportionate to the potential impact of technology.
- Establish ongoing monitoring of AI performance, effectiveness, risk indicators, incidents, exceptions and emerging threats.
- Partner with cybersecurity, Privacy, Legal, Compliance, Data Governance, and Technology Risk to address AI-specific risks.
- Establish governance for the use of external AI providers, foundation models, APIs, AI platforms, and third-party AI solutions.
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