A tailored course, built for your situation
Production-Grade AI Ethics for Product Management
Implement ethical AI systems with confidence in public-sector programs
$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 ethics guidance is too often abstract, what's missing is how to operationalize it in real product workflows.
The situation this course is for
Teams are expected to deliver AI-driven solutions quickly, but lack clear, actionable methods to ensure fairness, accountability, and compliance. Without implementation-grade tools, ethics becomes a checklist, not a capability.
Who this is for
Product managers, AI leads, and technology strategists in public-sector programs who must deliver compliant, trustworthy AI systems under real-world constraints.
Who this is not for
This is not for academics or theorists focused on philosophical AI ethics. It’s not for engineers seeking coding tutorials. It’s for practitioners who ship products and need repeatable, auditable processes.
What you walk away with
- Apply a structured framework to classify and mitigate AI risks in public-sector contexts
- Integrate bias detection and mitigation into product development lifecycle
- Map stakeholder impacts and build defensible decision records
- Align AI governance with compliance requirements including civil rights and procurement rules
- Operationalize ethics review into sprint planning and delivery workflows
The 12 modules (with all 144 chapters)
Module 1. Foundations of Production-Grade AI Ethics
Define what distinguishes operational ethics from theoretical frameworks in public-sector AI.
12 chapters in this module
- Defining production-grade vs. principle-based ethics
- Core expectations in public-sector AI delivery
- The role of product management in ethical governance
- Lifecycle view of AI ethics integration
- Compliance vs. trust: aligning objectives
- Regulatory touchpoints in public programs
- Common failure modes in AI ethics rollouts
- Stakeholder expectations across agencies
- Ethics as a delivery enabler, not a blocker
- Case study: AI in benefits eligibility
- Case study: predictive policing oversight
- Self-assessment: ethics maturity audit
Module 2. Risk Classification and Tiering
Learn to categorize AI applications by impact level and assign governance rigor accordingly.
12 chapters in this module
- High-impact vs. low-impact AI definitions
- Developing a risk tiering rubric
- Mapping use cases to risk bands
- Human autonomy thresholds
- Data sensitivity classification
- Public visibility and scrutiny factors
- Legal exposure indicators
- Equity impact scoring
- Dynamic risk reassessment triggers
- Case study: automated child welfare triage
- Case study: permit approval automation
- Template: AI risk classification worksheet
Module 3. Bias Detection and Mitigation Workflow
Implement a repeatable process for identifying and correcting bias in datasets and models.
12 chapters in this module
- Types of algorithmic bias in public programs
- Bias in data collection and labeling
- Representation auditing techniques
- Disaggregated outcome analysis
- Pre-processing vs. in-model corrections
- Post-deployment disparity testing
- Bias testing in non-binary categories
- Intersectional impact assessment
- Bias documentation standards
- Case study: hiring tool for civil service
- Case study: school placement algorithms
- Template: bias audit report
Module 4. Stakeholder Mapping and Engagement
Identify and involve the right parties at the right stages of AI development.
12 chapters in this module
- Primary vs. secondary stakeholders
- Mapping decision rights and concerns
- Public consultation design principles
- Community advisory board setup
- Transparency thresholds by risk level
- Communicating uncertainty and limitations
- Documentation for public access
- Handling dissent and objections
- Engagement timing in agile cycles
- Case study: public health AI rollout
- Case study: transportation equity modeling
- Template: stakeholder engagement log
Module 5. Ethics by Design in Product Lifecycle
Embed ethical reviews and checkpoints into standard product workflows.
12 chapters in this module
- Integrating ethics into discovery phase
- Ethics tollgates in sprint planning
- Checklist design for development teams
- Product owner responsibilities
- Definition of ethically 'done'
- Backlog prioritization with ethics weight
- Retrospective inclusion of ethics review
- Cross-functional ethics pairing
- Tooling integration: Jira, Asana, etc.
- Case study: digital service onboarding
- Case study: fraud detection system
- Template: ethics integration roadmap
Module 6. Compliance Integration
Align AI ethics practices with existing legal and regulatory frameworks.
12 chapters in this module
- Civil rights law applicability
- Procurement rule implications
- Accessibility standards for AI interfaces
- Data protection and retention rules
- Due process considerations
- Audit trail requirements
- Documentation for oversight bodies
- Interpreting guidance from OMB, GAO, etc.
- Cross-jurisdictional compliance
- Case study: unemployment claims processing
- Case study: housing assistance eligibility
- Template: compliance crosswalk matrix
Module 7. Model Transparency and Explainability
Provide meaningful explanations of AI decisions without compromising security or complexity.
12 chapters in this module
- Levels of explainability by use case
- Public-facing vs. internal explanations
- Right to explanation frameworks
- Simplified decision logic presentation
- Documentation for appeals processes
- Managing trade-offs with model accuracy
- Explainability in ensemble models
- Human-in-the-loop design patterns
- Language access and translation needs
- Case study: benefit denial notices
- Case study: permit denial appeals
- Template: model transparency summary
Module 8. Monitoring and Feedback Loops
Establish ongoing oversight to detect drift, harm, and performance degradation.
12 chapters in this module
- Key metrics for ethical performance
- Automated fairness monitoring
- Human feedback collection channels
- Error reporting pathways
- Performance decay detection
- Bias drift over time
- Public reporting dashboards
- Incident response protocols
- Model retraining triggers
- Case study: eviction prediction tool
- Case study: foster placement matching
- Template: monitoring dashboard spec
Module 9. Governance Structure and Roles
Define clear responsibilities for ethics oversight across teams and agencies.
12 chapters in this module
- AI ethics board composition
- Product manager as ethics steward
- Legal and compliance interface
- Oversight reporting lines
- Cross-agency coordination models
- Vendor ethics accountability
- Training requirements for roles
- Escalation pathways for concerns
- Documentation ownership
- Case study: interdepartmental health data use
- Case study: cross-jurisdictional benefit delivery
- Template: governance RACI matrix
Module 10. Documentation and Audit Readiness
Create clear, defensible records that support review and public trust.
12 chapters in this module
- AI documentation standards
- Model cards for public programs
- Decision logs and rationale capture
- Version control for ethics decisions
- Public records request preparedness
- Third-party audit access design
- Redaction and privacy balance
- Historical change tracking
- Archival requirements
- Case study: public records inquiry response
- Case study: legislative oversight review
- Template: audit readiness checklist
Module 11. Scaling Ethical Practices Across Portfolios
Extend implementation from one project to organization-wide capability.
12 chapters in this module
- Lessons from pilot to production
- Standardizing ethics tooling
- Training and enablement rollout
- Center of excellence models
- Metrics for ethics maturity
- Resource allocation strategies
- Leadership engagement tactics
- Budgeting for ethics activities
- Vendor ethics requirements
- Case study: city-wide AI inventory
- Case study: state agency ethics rollout
- Template: scaling roadmap
Module 12. Future-Proofing and Adaptive Governance
Prepare for evolving standards, technologies, and public expectations.
12 chapters in this module
- Anticipating regulatory changes
- Technology horizon scanning
- Public sentiment monitoring
- Ethics review of generative AI
- Adaptive policy drafting
- Scenario planning for AI futures
- Updating frameworks iteratively
- Balancing innovation and caution
- Exit strategies for harmful systems
- Case study: generative chatbot in benefits
- Case study: AI-assisted case management
- Template: adaptive governance calendar
How this maps to your situation
- You're launching AI pilots and need to scale them responsibly
- You're responding to oversight requirements with limited tools
- You're building internal capacity for AI governance
- You're leading digital transformation with ethical integrity
Before vs. after
Before
AI ethics feels abstract, reactive, and disconnected from delivery timelines.
After
Your team applies a structured, auditable process to embed ethics into product workflows from day one.
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 4 hours per module, designed to be completed alongside active projects. Total time: 48, 60 hours over 12 weeks.
If nothing changes
Without implementation-grade practices, organizations risk public backlash, audit findings, legal challenges, and loss of trust, even when intentions are good.
How this compares to the alternatives
Unlike academic courses or high-level policy guides, this program delivers implementation-grade tools for product managers who must deliver AI systems that are both innovative and accountable.
Frequently asked
Who is this course for?
Product managers, AI leads, and technology strategists in public-sector programs who need to deliver trustworthy AI systems with real-world constraints.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this technical or policy-focused?
It’s designed for practitioners who bridge both worlds, product managers and leaders who need actionable methods, not just theory.
$199 one-time. Approximately 4 hours per module, designed to be completed alongside active projects. Total time: 48, 60 hours over 12 weeks..
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