A tailored course, built for your situation
Board-Level AI Ethics for Product Management
Master ethical AI governance at scale with implementation-grade frameworks for high-growth tech organizations.
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
AI product leaders are expected to govern ethically but lack structured, board-ready frameworks to guide decisions.
The situation this course is for
As AI systems move into production at scale, product managers face growing pressure to demonstrate ethical rigor, without clear standards, tools, or governance workflows. Ambiguity leads to delays, compliance exposure, and misalignment between technical teams and executive leadership.
Who this is for
Product leaders, AI governance specialists, and technical executives in high-growth organizations scaling AI systems.
Who this is not for
This is not for entry-level contributors, hobbyists, or those seeking theoretical overviews of AI ethics without implementation focus.
What you walk away with
- Apply board-ready AI ethics frameworks aligned with global regulatory trends
- Lead cross-functional AI governance initiatives with confidence
- Integrate ethical decision-making into product development lifecycles
- Build investor-grade compliance documentation for AI deployments
- Anticipate and mitigate reputational and operational risks in AI scaling
The 12 modules (with all 144 chapters)
Module 1. The Evolution of AI Ethics in Product Leadership
Trace the shift from ethics as principle to ethics as operational governance.
12 chapters in this module
- From aspiration to accountability in AI systems
- Mapping stakeholder expectations across functions
- Regulatory signals shaping current frameworks
- Investor scrutiny and ESG alignment
- Case study: Governance failure post-launch
- Case study: Proactive ethics enabling scale
- Defining 'ethical debt' in product contexts
- The board’s growing role in AI oversight
- Benchmarking maturity across peer organizations
- Emerging expectations for product leaders
- Common misconceptions about AI ethics
- Setting the foundation for implementation
Module 2. Governance Models for High-Growth AI Organizations
Compare and select governance structures that scale with organizational complexity.
12 chapters in this module
- Centralized vs. federated governance models
- Role of the AI ethics review board
- Integrating legal and compliance functions
- Engineering team responsibilities
- Product manager as ethics steward
- Escalation pathways for edge cases
- Documentation standards for decisions
- Versioning ethical policies over time
- Auditing AI governance workflows
- Metrics for governance effectiveness
- Managing external auditor expectations
- Adapting models for international operations
Module 3. AI Risk Taxonomy for Product Teams
Classify and prioritize AI risks with precision across technical and social dimensions.
12 chapters in this module
- Defining harm types: individual, societal, systemic
- Bias across data, model, and deployment
- Transparency vs. obfuscation tradeoffs
- Privacy implications in training data
- Security vulnerabilities in AI systems
- Reputational exposure from misuse
- Environmental cost of AI infrastructure
- Labor displacement and economic impact
- Cultural appropriation in generative models
- Long-term societal feedback loops
- Risk scoring frameworks for product review
- Creating a living risk register
Module 4. Ethical Decision Frameworks for Product Development
Implement structured decision-making tools for AI product lifecycle stages.
12 chapters in this module
- Pre-mortem analysis for AI features
- Stakeholder mapping for ethical impact
- Consent models for data usage
- Fairness metrics by use case
- Explainability requirements by audience
- Human-in-the-loop design patterns
- Fallback mechanisms for failure modes
- Red teaming AI product concepts
- Scenario planning for unintended use
- Cost of delay in ethical review
- Balancing innovation and caution
- Decision logs for audit readiness
Module 5. Compliance Architecture for Global AI Regulations
Build compliance-ready systems aligned with major regulatory regimes.
12 chapters in this module
- EU AI Act: obligations for product teams
- U.S. federal and state-level guidance
- UK AI governance standards
- Canada’s Algorithmic Impact Assessment
- Singapore’s Model AI Governance Framework
- Japan’s Social Principles of AI
- China’s algorithm registration rules
- Cross-border data and model deployment
- Sector-specific rules: health, finance, education
- Recordkeeping for regulatory audits
- Third-party model compliance
- Updating policies as regulations evolve
Module 6. AI Ethics Integration into Product Lifecycle
Embed ethical review at every stage from ideation to post-launch.
12 chapters in this module
- Ethics checkpoints in sprint planning
- Integrating ethics into PRDs
- Design sprints with harm modeling
- Ethical QA testing protocols
- Staged rollout with monitoring
- Feedback loops from end users
- Incident response for AI failures
- Post-mortems with ethics focus
- Version control for model updates
- Deprecation of unethical features
- Scaling ethical practices across teams
- Automation of ethics checks
Module 7. Stakeholder Communication for AI Governance
Develop messaging strategies for executives, boards, and external parties.
12 chapters in this module
- Translating technical risk for non-technical leaders
- Board reporting templates
- Investor-facing ethics narratives
- Press and public response frameworks
- Internal comms for employee trust
- Handling whistleblower concerns
- Engaging civil society groups
- Responding to media scrutiny
- Building public credibility
- Crisis communication playbooks
- Managing activist investor pressure
- Narrative consistency across channels
Module 8. Metrics and Monitoring for Ethical AI Systems
Define and track KPIs that reflect ethical performance over time.
12 chapters in this module
- Defining success beyond accuracy
- Bias tracking across demographics
- User trust and perception metrics
- Complaint resolution timelines
- Model drift and fairness degradation
- Audit frequency and coverage
- Incident rate and severity trends
- Ethics debt tracking
- Team sentiment on ethical dilemmas
- Third-party audit outcomes
- Public sentiment analysis
- Benchmarking against industry peers
Module 9. AI Ethics Training for Cross-Functional Teams
Equip product, engineering, and support teams with shared ethical practices.
12 chapters in this module
- Onboarding for AI ethics standards
- Role-specific training modules
- Scenario-based learning for teams
- Certification within organization
- Gamification of ethical decision-making
- Refresher cycles and updates
- Leadership training for managers
- Mentorship programs
- Feedback mechanisms for training
- Assessing knowledge retention
- Scaling training across regions
- Integrating with performance reviews
Module 10. AI Incident Response and Remediation
Prepare for and respond to AI failures with ethical accountability.
12 chapters in this module
- Defining AI incidents vs. outages
- Triage protocols for ethical breaches
- Internal investigation frameworks
- User notification strategies
- Remediation workflows
- Compensation and redress models
- Public apology frameworks
- Legal exposure mitigation
- Regulatory reporting obligations
- Lessons learned documentation
- Systemic fixes vs. one-off patches
- Rebuilding trust post-incident
Module 11. Third-Party and Supply Chain AI Governance
Extend ethical standards to vendors, partners, and open-source dependencies.
12 chapters in this module
- Vendor due diligence for AI tools
- Contractual ethics clauses
- Auditing third-party model behavior
- Open-source model provenance
- Attribution and licensing compliance
- Monitoring downstream misuse
- Liability allocation frameworks
- Responsible API usage policies
- Partner co-development ethics
- Exit strategies for unethical providers
- Global supply chain variations
- Enforcement of ethical clauses
Module 12. Scaling AI Ethics in High-Growth Organizations
Design systems that evolve with organizational growth and complexity.
12 chapters in this module
- From ad-hoc to institutionalized ethics
- Hiring for AI ethics roles
- Budgeting for governance functions
- Tooling investments for scale
- Executive sponsorship models
- Board-level reporting cadence
- M&A due diligence for AI ethics
- Global expansion challenges
- Cultural adaptation of frameworks
- Public leadership in AI ethics
- Thought leadership and publishing
- Contributing to industry standards
How this maps to your situation
- You're launching AI features with increasing scrutiny
- You're building internal AI governance processes
- You're preparing for regulatory audits
- You're scaling AI systems across markets
Before vs. after
Before
Uncertain how to structure AI ethics decisions or communicate them to executives and boards.
After
Confidently lead AI governance initiatives with board-ready frameworks, clear documentation, and stakeholder alignment.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 48 hours total, designed for self-paced completion over six weeks with weekly milestones.
If nothing changes
Without structured AI ethics governance, organizations face increased regulatory scrutiny, reputational damage, loss of investor confidence, and operational delays in AI deployment.
How this compares to the alternatives
Unlike generic AI ethics courses, this program is tailored to product leaders in high-growth organizations, offering implementation-grade tools, real-world templates, and board-focused communication strategies not found in academic or awareness-only programs.
Frequently asked
Who is this course designed for?
Product leaders, AI governance specialists, and technical executives in organizations scaling AI systems with board-level oversight.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or strategic?
It balances both: strategic frameworks for leadership and technical implementation tools for product teams.
$199 one-time. Approximately 48 hours total, designed for self-paced completion over six weeks with weekly milestones..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours