A tailored course, built for your situation
Audit-Tested AI Ethics for Product Management for Regulated Industries
Implement ethical, compliant AI systems with confidence in high-stakes environments
$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.
Deploying AI in a regulated context without a clear audit trail creates inefficiencies, delays, and reputational exposure during compliance reviews
The situation this course is for
Product managers in financial services, healthcare, and public-sector institutions are increasingly held accountable for AI system behavior, but most lack a structured, repeatable method to align model development with compliance requirements from day one. Traditional ethics training stops at principles; this leaves teams scrambling during audits, rewriting documentation, or delaying launches due to governance gaps.
Who this is for
Product managers, compliance leads, and technology officers in regulated industries who are responsible for launching or overseeing AI-driven products and need to ensure ethical design is operationalized, not just theorized
Who this is not for
This course is not for developers seeking to learn machine learning coding techniques, nor for executives wanting only a high-level overview of AI trends. It is also not for organizations without formal compliance or audit processes, or those not currently deploying or planning AI in regulated environments.
What you walk away with
- Apply a structured, audit-ready framework to AI product design from concept to deployment
- Map AI system decisions to regulatory expectations and documentation standards
- Reduce review cycle time by pre-building compliance artifacts into development workflows
- Lead cross-functional teams with confidence using shared ethical implementation criteria
- Anticipate auditor questions and preemptively address gaps in AI governance documentation
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Ethics in Regulated Contexts
Establish core definitions, regulatory drivers, and the business case for audit-ready AI ethics in product development
12 chapters in this module
- Defining audit-tested AI ethics
- Regulatory landscape overview
- Sector-specific risk profiles
- The product manager's evolving role
- Ethics vs. compliance: aligning intent
- Stakeholder mapping for governance
- Lifecycle view of AI accountability
- Common pitfalls in early design
- Documentation as a product feature
- Internal audit expectations
- Third-party assessment criteria
- Building cross-functional alignment
Module 2. Regulatory Alignment Across Jurisdictions
Compare major frameworks including GDPR, HIPAA, NIST AI RMF, and sector-specific mandates to identify overlapping requirements
12 chapters in this module
- GDPR and automated decision-making
- HIPAA and health data systems
- NIST AI Risk Management Framework
- SEC expectations for AI disclosures
- State-level privacy laws alignment
- Cross-border data flow implications
- Sector-specific enforcement trends
- Regulatory overlap analysis
- Harmonizing compliance across regions
- Audit trigger points by jurisdiction
- Regulator communication protocols
- Preparing for inspection readiness
Module 3. Designing for Auditability from Inception
Embed compliance into product requirements, user stories, and technical specifications from day one
12 chapters in this module
- Auditability as a product requirement
- Traceability in data sourcing
- Model decision logging standards
- Version-controlled ethics documentation
- Data lineage for AI systems
- Human oversight integration
- Bias assessment integration
- Transparency by design
- Stakeholder feedback loops
- Consent and opt-out mechanisms
- Explainability thresholds
- Pre-audit self-assessment checklist
Module 4. Risk-Based AI Categorization Frameworks
Classify AI systems by risk level to allocate governance resources effectively and prioritize audit readiness
12 chapters in this module
- High-risk AI definitions
- Medium and low-risk categorization
- Dynamic risk reassessment
- Regulatory threshold triggers
- Internal risk scoring model
- Documentation depth by tier
- Resource allocation strategies
- Escalation protocols
- Third-party review thresholds
- Model lifecycle governance
- Change management for AI updates
- Sunset and deprecation planning
Module 5. Bias Detection and Mitigation in Practice
Implement technical and procedural safeguards to identify, document, and reduce bias in AI systems
12 chapters in this module
- Defining fairness in context
- Bias types in training data
- Algorithmic fairness metrics
- Pre-processing mitigation techniques
- In-model fairness constraints
- Post-processing adjustments
- Disparate impact testing
- Demographic parity analysis
- Bias audit documentation
- Ongoing monitoring systems
- Stakeholder review processes
- Remediation playbooks
Module 6. Transparency and Explainability Standards
Develop clear, audience-appropriate explanations of AI decisions for users, regulators, and internal stakeholders
12 chapters in this module
- Levels of explainability
- User-facing transparency
- Regulator-facing documentation
- Technical vs. layperson explanations
- Model cards for transparency
- Fact sheets for AI systems
- Local vs. global interpretability
- SHAP and LIME in practice
- Confidence interval reporting
- Uncertainty communication
- Right to explanation compliance
- Explainability testing protocols
Module 7. Data Provenance and Governance
Establish robust data sourcing, labeling, and versioning practices that support audit defense
12 chapters in this module
- Data lineage tracking
- Training data documentation
- Labeling process audits
- Data quality benchmarks
- Version control for datasets
- Consent verification systems
- Third-party data compliance
- Data retention policies
- Data minimization techniques
- Anonymization standards
- Re-identification risk assessment
- Data governance workflows
Module 8. Human-in-the-Loop and Oversight Design
Architect meaningful human review points and escalation paths for AI-driven decisions
12 chapters in this module
- When to require human review
- Escalation threshold design
- Reviewer role definition
- Training for human reviewers
- Review interface design
- Intervention logging
- Feedback to model retraining
- Oversight dashboard development
- Review frequency planning
- False positive/negative tracking
- Audit trail for overrides
- Performance monitoring for reviewers
Module 9. Incident Response and Model Monitoring
Build systems to detect, log, and respond to AI performance drift and ethical incidents
12 chapters in this module
- Model performance thresholds
- Drift detection mechanisms
- Ethical incident definition
- Internal reporting pathways
- Escalation protocols
- Root cause analysis
- Remediation workflows
- Stakeholder communication
- Regulatory reporting triggers
- Post-incident review
- Model rollback procedures
- Continuous monitoring tools
Module 10. Third-Party AI and Vendor Management
Apply audit-tested ethics standards to externally developed or hosted AI systems
12 chapters in this module
- Vendor risk assessment
- Contractual compliance terms
- Audit rights negotiation
- Third-party documentation standards
- Model transparency requirements
- Performance monitoring SLAs
- Ethical alignment verification
- Onboarding review process
- Ongoing oversight mechanisms
- Exit strategy planning
- Liability allocation
- Joint incident response planning
Module 11. Cross-Functional Governance Models
Establish clear roles, responsibilities, and decision rights across product, legal, compliance, and engineering
12 chapters in this module
- AI governance committee structure
- RACI matrix for AI projects
- Cross-departmental workflows
- Escalation paths
- Decision logging
- Policy alignment
- Training and enablement
- Compliance monitoring
- Audit preparation roles
- Resource allocation
- Conflict resolution
- Continuous improvement
Module 12. Preparing for Regulatory Audits
Assemble and maintain documentation packages that satisfy current and anticipated audit requirements
12 chapters in this module
- Audit checklist development
- Document repository structure
- Evidence collection protocols
- Internal dry-run audits
- Regulator communication plan
- Response coordination
- Gap remediation
- Corrective action tracking
- Audit follow-up
- Lessons learned integration
- Continuous readiness
- Stakeholder briefing
How this maps to your situation
- Launching AI products in regulated environments
- Facing internal or external audit of AI systems
- Designing governance frameworks for AI oversight
- Responding to regulatory inquiries or investigations
Before vs. after
Before
Uncertain about how to structure AI ethics in a way that satisfies both product goals and compliance requirements, leading to delayed launches and reactive documentation
After
Confidently lead AI product initiatives with built-in audit readiness, standardized ethical implementation, and cross-functional 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 4-6 hours per module, designed to be completed at your pace with just-in-time implementation tools.
If nothing changes
Organizations that delay implementing structured AI ethics frameworks face increased scrutiny, longer approval cycles, and higher remediation costs during audits, along with reputational risk from publicized compliance failures.
How this compares to the alternatives
Unlike generic AI ethics courses or one-size-fits-all compliance training, this program delivers a product management-specific, audit-tested framework with templates and playbooks tailored to regulated industry needs, making it actionable from day one.
Frequently asked
Who is this course designed for?
Product managers, compliance officers, and technology leaders in regulated industries who are responsible for launching or overseeing AI systems and need to ensure ethical and compliant design.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there hands-on work included?
Yes, each module includes downloadable templates, real-world examples, and implementation checklists to apply concepts directly to your work.
$199 one-time. Approximately 4-6 hours per module, designed to be completed at your pace with just-in-time implementation tools..
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