Govern AI before it governs you
A practitioner-grade program for the senior leaders and risk officers who must answer to boards, regulators, and auditors — built around the frameworks, validation standards, and regulatory fluency that enterprise ML governance actually demands.
"The leaders who govern AI with precision and accountability will define what responsible enterprise looks like in the next decade — and I built this program to put you in that position."— Pauline Brown Smith, EdD
What you'll learn
What you'll be able to do
- Build and implement an enterprise ML governance framework with clear executive accountability, lifecycle controls, and board-level reporting structures.
- Design an independent model risk management program covering validation, change control, incident response, and ongoing monitoring.
- Apply ethical AI principles — fairness, transparency, accountability, and privacy — to real governance policies and model deployment decisions.
- Construct a model validation and assurance scorecard that evaluates performance, bias, robustness, and explainability across the full model lifecycle.
- Identify and operationalize compliance obligations across sector-specific AI regulations and global data privacy laws within an enterprise risk register.
- Deliver a five-year ML governance roadmap and executive KPI dashboard that drives continuous improvement and demonstrates regulatory readiness to auditors and boards.
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 29 lessons
Machine Learning Foundations for Executive Leaders
Equips senior leaders with the conceptual and strategic ML literacy needed to govern AI systems confidently at the enterprise level.
- 1.1How Machine Learning Works: An Executive Mental ModelIncluded
- 1.2Deep Learning and Intelligent Decision Systems in the EnterpriseIncluded
- 1.3The ML Model Lifecycle: From Data to Deployment to RetirementIncluded
- 1.4Distinguishing Established ML Practice from Emerging ResearchIncluded
Enterprise ML Governance Framework
Builds the governance architecture — accountability structures, policies, and board reporting — that regulators and auditors expect.
- 2.1Executive Accountability and Governance Structures for MLIncluded
- 2.2ML Data Governance: Quality, Provenance, Privacy, and SecurityIncluded
- 2.3Model Documentation Standards and Change ControlIncluded
- 2.4Human Oversight Mechanisms and Cybersecurity ControlsIncluded
- 2.5Building the ML Governance Roadmap and Continuous Improvement CycleIncluded
Model Risk Management
Designs an independent, end-to-end model risk management program that controls risk from model inception through incident response.
- 3.1Model Risk Management Principles and Program ArchitectureIncluded
- 3.2Independent Model Validation: Process, Standards, and Red LinesIncluded
- 3.3Ongoing Model Monitoring and Performance SurveillanceIncluded
- 3.4Incident Response, Model Failure Management, and Root Cause AnalysisIncluded
- 3.5The Enterprise Risk Register for ML: Identification, Scoring, and ReportingIncluded
Ethical AI: Governance Policies and Deployment Decisions
Translates fairness, transparency, accountability, and privacy principles into enforceable governance policies and deployment controls.
- 4.1Ethical AI Principles and Their Governance ImplicationsIncluded
- 4.2Operationalizing Fairness: Bias Identification and Mitigation PoliciesIncluded
- 4.3Transparency and Explainability as Governance RequirementsIncluded
- 4.4Privacy by Design and Data Ethics in ML SystemsIncluded
- 4.5Embedding Ethical AI into Model Deployment and Procurement DecisionsIncluded
Model Validation, Assurance, and the Regulatory Landscape
Constructs a rigorous model validation and assurance scorecard and maps it directly to global AI and data privacy regulatory obligations.
- 5.1Designing the Model Validation and Assurance ScorecardIncluded
- 5.2Robustness, Stress Testing, and Adversarial Resilience EvaluationIncluded
- 5.3AI Regulatory Frameworks: Finance, Healthcare, and InsuranceIncluded
- 5.4Global Data Privacy Laws and ML Compliance ObligationsIncluded
- 5.5ML Assurance Reviews: Conducting Structured Executive AssessmentsIncluded
Executive Leadership, Board Reporting, and the ML Governance Portfolio
Integrates all prior learning into board-ready deliverables, the executive KPI dashboard, and a capstone governance initiative.
- 6.1The Executive KPI Dashboard for ML GovernanceIncluded
- 6.2Communicating ML Risk and Governance to Boards and RegulatorsIncluded
- 6.3Stakeholder Collaboration and Cross-Functional Governance AlignmentIncluded
- 6.4The Five-Year ML Governance Transformation RoadmapIncluded
- 6.5Capstone: Designing Your Enterprise ML Governance InitiativeIncluded
Who it's for
Is this you?
Chief Risk Officers
Build the MRM program architecture, validation standards, and board-level risk reporting structures that ML systems in your enterprise actually require.
Chief Compliance Officers
Map your regulatory exposure across sector-specific AI frameworks and global data privacy laws, and operationalize compliance obligations into a defensible enterprise risk register.
AI Strategy Executives
Anchor your AI transformation agenda in the governance accountability structures and five-year roadmap that boards and regulators will demand as a condition of confidence.
Model Risk Directors
Establish independent validation processes, change control standards, and incident response protocols that hold up under regulatory scrutiny and internal audit.
Data Governance Leaders
Embed data quality, provenance, privacy by design, and security controls into ML governance frameworks that satisfy both internal policy and external obligation.
Senior Compliance Officers in Regulated Industries
Translate fairness, explainability, and ethical AI principles into enforceable governance policies that protect the organization at the point of model deployment and procurement.
Questions
Frequently asked
Your teacher
A note from your teacher
Pauline Brown Smith, EdD
If you're reading this, you're likely already responsible for AI decisions that your organization — and possibly its regulators — do not yet have adequate governance structures to support. You may have inherited a patchwork of policies. You may be fielding board questions you don't have clean answers to. You may be watching the regulatory environment harden in real time while your internal frameworks lag behind. That position is uncomfortable, and it carries real professional and institutional risk.
I've spent my career working at exactly that intersection — where strategic ambition meets regulatory accountability, where the pressure to deploy AI faster collides with the obligation to deploy it safely. What I've learned is that the leaders who navigate this well are not the ones who understand the most mathematics. They are the ones who have clear governance structures, rigorous validation standards, enforceable ethical policies, and the ability to communicate ML risk in language that boards and regulators actually trust. This school is built to give you all of that.
The curriculum is deliberately practitioner-grade. We begin with the executive mental model of ML — not to teach you to build models, but to give you the conceptual precision to govern them. From there we move through enterprise governance frameworks with explicit accountability structures, independent model risk management programs with defined validation standards and incident response protocols, and ethical AI principles translated into enforceable deployment and procurement policy. We work through the regulatory landscape across finance, healthcare, and insurance with enough rigour to build a real compliance posture — not a slide deck.
The program closes where your accountability lives: board reporting, executive KPI dashboards, stakeholder alignment, and a five-year governance transformation roadmap. The capstone is your enterprise ML governance initiative — a real deliverable, designed for your specific context.
I will not pretend that governance is glamorous work. But I will tell you this: the leaders who establish credible ML governance programs in the next few years will hold a structural advantage — with their boards, with their regulators, and with the markets that are beginning to price AI risk with considerably more sophistication. This is the moment to build what is required. This school gives you the framework, the tools, and the rigour to do it.
— Pauline Brown Smith, EdD
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- 6 modules, 29 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed