A tailored course, built for your situation
Pragmatic AI Ethics for Product Management for Senior Leaders
Implement ethical AI frameworks with confidence and clarity in real-world product leadership roles
$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.
Navigating AI ethics without slowing innovation or inviting risk
The situation this course is for
Senior product leaders face mounting pressure to deploy AI responsibly, but lack practical frameworks to balance ethical standards with delivery timelines, stakeholder demands, and regulatory expectations. Without structured guidance, teams default to reactive compliance or over-index on caution, stalling progress.
Who this is for
Senior product managers, technology directors, and innovation leads in regulated or data-sensitive sectors who influence AI product strategy and governance.
Who this is not for
Individual contributors without decision authority, entry-level product staff, or practitioners seeking theoretical AI ethics discourse without implementation focus.
What you walk away with
- Apply a structured framework to assess and mitigate ethical risks in AI product design
- Align cross-functional teams around shared principles for responsible AI deployment
- Integrate compliance requirements into product roadmaps without sacrificing agility
- Build stakeholder trust through transparent AI governance practices
- Lead with confidence in board-level conversations about AI responsibility and impact
The 12 modules (with all 144 chapters)
Module 1. Foundations of Pragmatic AI Ethics
Establish core principles and scope for ethical AI in product leadership
12 chapters in this module
- Defining pragmatic ethics in AI
- Distinguishing ethics from compliance
- The product leader’s role in ethical governance
- Mapping stakeholder expectations
- Ethical debt and technical debt parallels
- Balancing innovation with responsibility
- Case study: healthcare AI triage tool
- Identifying red lines and green zones
- Common misconceptions about AI ethics
- Ethics as a competitive advantage
- Frameworks for scalable decision-making
- Introducing the implementation playbook
Module 2. Regulatory Landscape and Industry Standards
Understand current expectations and emerging norms across jurisdictions
12 chapters in this module
- Global regulatory trends in AI
- Healthcare-specific AI guidelines
- GDPR and AI implications
- FDA considerations for AI-enabled tools
- NIST AI Risk Management Framework
- OECD AI Principles in practice
- Sector-specific compliance benchmarks
- Anticipating future rulemaking
- Mapping regulations to product lifecycle
- Gap analysis techniques
- Preparing for audits
- Staying ahead of enforcement trends
Module 3. Bias Identification and Mitigation
Detect and address bias across data, models, and outcomes
12 chapters in this module
- Sources of algorithmic bias
- Data provenance and representation
- Feature selection risks
- Labeling bias in training sets
- Demographic parity assessment
- Disparate impact analysis
- Bias detection tools overview
- Mitigation strategies by phase
- Trade-offs between fairness metrics
- Monitoring in production
- Stakeholder communication about bias
- Documenting mitigation efforts
Module 4. Transparency and Explainability
Build trust through clear communication of AI behavior
12 chapters in this module
- Levels of explainability required
- Model cards and datasheets
- User-facing transparency needs
- Internal documentation standards
- Simplifying complexity without distortion
- Right to explanation frameworks
- Trade-offs with IP protection
- Explainability in clinical contexts
- Tools for model interpretation
- Auditing for clarity
- Managing user expectations
- Versioning transparency artifacts
Module 5. Accountability Structures
Define roles, responsibilities, and escalation paths
12 chapters in this module
- RACI models for AI governance
- Ethics review board design
- Product-level accountability
- Escalation protocols for issues
- Incident response planning
- Post-deployment monitoring ownership
- Vendor accountability frameworks
- Third-party audit readiness
- Liability considerations
- Documenting decisions
- Whistleblower protections
- Culture of psychological safety
Module 6. Risk Assessment and Management
Integrate ethical risk evaluation into product workflows
12 chapters in this module
- AI-specific risk taxonomies
- Harm potential scoring
- Likelihood-impact matrices
- Risk register maintenance
- Integrating risk assessment into sprints
- Pre-mortem techniques
- Scenario planning for edge cases
- Thresholds for escalation
- Risk communication templates
- Updating assessments dynamically
- Linking risk to OKRs
- Reporting to executive leadership
Module 7. Human-in-the-Loop Design
Ensure appropriate human oversight in AI systems
12 chapters in this module
- Levels of automation and oversight
- Designing for human intervention
- Alert fatigue mitigation
- Feedback loop integration
- Calibrating trust in AI outputs
- Training for human reviewers
- Workload implications
- Fallback procedure design
- Monitoring human-AI handoffs
- Audit trails for decisions
- Scalability of oversight models
- Cost-benefit of human involvement
Module 8. Consent and Data Governance
Handle data rights and permissions with rigor
12 chapters in this module
- Informed consent in AI contexts
- Data lineage tracking
- Purpose limitation enforcement
- Patient data rights under HIPAA
- Data minimization techniques
- Anonymization effectiveness
- Re-consent triggers
- Data access request workflows
- Vendor data handling standards
- Data retention policies
- Audit readiness for data practices
- Consent documentation frameworks
Module 9. Equity and Access Considerations
Ensure AI systems serve diverse populations fairly
12 chapters in this module
- Defining equity in healthcare AI
- Access disparities analysis
- Language and literacy considerations
- Disability inclusion in design
- Rural vs. urban access gaps
- Cost as a barrier to access
- Cultural competency requirements
- Community engagement strategies
- Measuring differential outcomes
- Feedback mechanisms for underserved groups
- Bias in user experience design
- Reporting on equity metrics
Module 10. Long-Term Monitoring and Adaptation
Sustain ethical performance over time
12 chapters in this module
- Performance drift detection
- Feedback collection systems
- Model retraining triggers
- Concept drift identification
- Updating documentation
- Version control for ethics artifacts
- Stakeholder updates
- Handling model sunsetting
- Post-market surveillance
- Adapting to new evidence
- Continuous improvement cycles
- Decommissioning protocols
Module 11. Stakeholder Communication
Navigate conversations with executives, regulators, and users
12 chapters in this module
- Tailoring messages by audience
- Board-level reporting templates
- Regulator engagement strategies
- User communication best practices
- Crisis communication planning
- Managing media inquiries
- Internal comms for teams
- Vendor communication standards
- Building public trust
- Responding to criticism
- Proactive disclosure frameworks
- Maintaining transparency over time
Module 12. Scaling Ethical Practices
Expand responsible AI across the organization
12 chapters in this module
- Pilot to production ethics
- Cross-product consistency
- Center of excellence models
- Training programs for teams
- Knowledge sharing mechanisms
- Tooling standardization
- Budgeting for ethics work
- Measuring maturity progression
- Benchmarking against peers
- External validation opportunities
- Sustaining leadership focus
- Future-proofing your approach
How this maps to your situation
- Product leaders launching first AI feature
- Teams scaling AI across multiple products
- Organizations responding to regulatory scrutiny
- Leaders preparing for board-level AI governance
Before vs. after
Before
Uncertain how to balance innovation with responsibility, relying on ad-hoc ethics reviews and reactive compliance.
After
Confidently lead AI initiatives with a structured, repeatable framework that builds trust, reduces risk, and accelerates responsible deployment.
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 3 hours per module, designed for completion in 12 weeks with flexible pacing.
If nothing changes
Without a pragmatic approach to AI ethics, product leaders risk delayed launches, regulatory challenges, erosion of patient trust, and reputational impact, all of which can undermine the long-term success of AI initiatives.
How this compares to the alternatives
Unlike academic courses focused on theory or broad overviews lacking implementation detail, this program provides actionable frameworks specifically designed for senior product leaders operating in regulated environments who need to deliver results without compromising ethical standards.
Frequently asked
Who is this course designed for?
Senior product managers, technology directors, and innovation leads in regulated or data-sensitive sectors who influence AI product strategy and governance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is awarded and can be shared internally or on professional networks.
$199 one-time. Approximately 3 hours per module, designed for completion in 12 weeks with flexible pacing..
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