A tailored course, built for your situation
Practical AI Ethics for Product Management for Compliance Officers
Master ethical AI integration in product development with actionable frameworks for compliance-first outcomes.
$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.
Compliance officers are being asked to assess AI risks without clear frameworks or practical playbooks.
The situation this course is for
AI product initiatives often outpace governance. Compliance teams face pressure to provide guidance without sufficient context, tools, or influence. This leads to reactive oversight, strained cross-functional relationships, and uncertainty in audit readiness.
Who this is for
Compliance, risk, and governance professionals in technology-driven organizations who need to guide ethical AI product development with confidence and precision.
Who this is not for
This is not for data scientists focused on model development or product managers seeking high-level AI overviews.
What you walk away with
- Apply a tiered risk framework to evaluate AI products pre-development
- Create audit-ready documentation for AI compliance across jurisdictions
- Lead cross-functional alignment between product, legal, and engineering teams
- Anticipate regulatory expectations using real-world case patterns
- Deploy a customizable implementation playbook for ongoing AI governance
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Ethics in Product Development
Establish core principles and compliance touchpoints in AI product lifecycles.
12 chapters in this module
- Defining ethical AI in regulated environments
- Compliance officer roles in product governance
- Mapping AI use cases to risk categories
- Key regulatory touchpoints by region
- Integrating ethics into product charters
- Stakeholder alignment fundamentals
- AI transparency expectations
- Bias detection thresholds
- Data provenance in product design
- Version control for ethical review
- Documenting intent and limitations
- Ethics by design vs. ethics by audit
Module 2. Regulatory Landscape for AI-Driven Products
Navigate evolving standards and compliance expectations across jurisdictions.
12 chapters in this module
- Global AI regulation trends
- EU AI Act compliance pathways
- US sector-specific guidance
- UK governance frameworks
- Asia-Pacific regulatory approaches
- Cross-border data flow implications
- Sector-specific obligations (finance, health, HR)
- Regulator engagement strategies
- Future-looking policy indicators
- Interpreting soft law and guidance
- Compliance timing across product phases
- Jurisdictional overlap management
Module 3. Risk Tiering for AI Product Portfolios
Classify AI applications by compliance risk level to prioritize oversight.
12 chapters in this module
- High-risk vs. limited-risk AI definitions
- Building a risk-tiering matrix
- Scoring model inputs and outputs
- Human-in-the-loop requirements
- Autonomy level assessment
- Impact on individual rights
- Third-party model accountability
- Supply chain transparency
- Failure mode analysis
- Escalation pathways for high-risk cases
- Documentation standards by tier
- Periodic reclassification protocols
Module 4. Compliance Integration in Product Roadmaps
Embed compliance checkpoints into agile development workflows.
12 chapters in this module
- Integrating compliance into sprint planning
- Pre-release review gates
- Compliance user story patterns
- Backlog prioritization with risk context
- Sprint zero compliance activities
- Definition of done with ethics criteria
- Compliance debt tracking
- Product team accountability models
- Compliance KPIs for product teams
- Escalation triggers for non-compliance
- Cross-functional sprint reviews
- Post-launch compliance monitoring
Module 5. Bias Detection and Mitigation in Product Design
Identify and address bias in data, models, and user experience.
12 chapters in this module
- Sources of bias in training data
- Demographic parity testing
- Disparate impact analysis
- User interface bias patterns
- Feedback loop distortions
- Bias testing pre-deployment
- Remediation strategies by type
- Bias disclosure standards
- Third-party audit coordination
- Ongoing monitoring for drift
- Bias reporting templates
- Stakeholder communication plans
Module 6. Transparency and Explainability Requirements
Meet disclosure expectations for internal and external stakeholders.
12 chapters in this module
- Levels of explainability by risk tier
- Model cards for internal use
- Public-facing disclosure standards
- User-facing explanations
- Technical documentation templates
- Right to explanation frameworks
- Summarizing model behavior simply
- Explainability testing methods
- Third-party verification readiness
- Version-to-version change logs
- Audit trail requirements
- Stakeholder-specific reporting
Module 7. Data Governance in AI Product Lifecycles
Ensure data provenance, quality, and compliance throughout development.
12 chapters in this module
- Data lineage tracking methods
- Training data documentation
- Data quality thresholds
- Consent verification in pipelines
- Data retention in model contexts
- Anonymization effectiveness
- Data subject rights fulfillment
- Data access controls
- Data versioning for audit
- Data bias auditing
- Data sourcing disclosures
- Data governance tool integration
Module 8. Human Oversight and Control Mechanisms
Design appropriate human-in-the-loop safeguards for AI products.
12 chapters in this module
- Defining meaningful human control
- Escalation threshold design
- Human review workflow integration
- Override capability standards
- Monitoring for automation bias
- Training for human reviewers
- Fallback mode requirements
- Response time expectations
- Auditability of human decisions
- Workload impact assessment
- Human-AI handoff design
- Oversight effectiveness metrics
Module 9. Third-Party and Supply Chain Compliance
Manage compliance risks from external AI components and vendors.
12 chapters in this module
- Vendor risk assessment frameworks
- Third-party model documentation
- Contractual compliance clauses
- Audit rights negotiation
- Model provenance verification
- Sub-processor transparency
- Open-source model compliance
- API-level compliance checks
- Vendor performance monitoring
- Exit strategy planning
- Due diligence checklists
- Ongoing compliance validation
Module 10. Incident Response and Remediation Planning
Prepare for AI-related incidents with structured response protocols.
12 chapters in this module
- Defining AI incidents and near-misses
- Incident classification frameworks
- Response team composition
- Notification timelines
- Remediation playbooks
- Root cause analysis methods
- Systemic failure pattern tracking
- Regulatory reporting obligations
- Public communication strategies
- Post-mortem documentation
- Corrective action tracking
- Preventive control updates
Module 11. Audit Readiness and Documentation Standards
Build and maintain compliance artifacts for internal and external review.
12 chapters in this module
- AI product compliance dossier structure
- Regulator-facing documentation
- Internal audit preparation
- Evidence collection frameworks
- Version control for compliance docs
- Cross-jurisdictional alignment
- Document retention policies
- Third-party audit coordination
- Compliance metadata tagging
- Automated compliance reporting
- Gap analysis templates
- Continuous improvement cycles
Module 12. Scaling AI Ethics Across Product Portfolios
Extend compliance practices across multiple teams and initiatives.
12 chapters in this module
- Centralized vs. embedded compliance models
- Compliance champion networks
- Standardized tooling rollout
- Cross-team knowledge sharing
- Maturity assessment frameworks
- Leadership reporting structures
- Budgeting for compliance functions
- Training program development
- Policy harmonization strategies
- Technology stack integration
- Performance measurement
- Continuous governance improvement
How this maps to your situation
- When launching a new AI product under regulatory scrutiny
- When responding to internal audit findings on AI governance
- When onboarding third-party AI models with compliance uncertainty
- When scaling AI initiatives across multiple business units
Before vs. after
Before
Uncertain how to apply compliance principles to fast-moving AI product development.
After
Equipped with a structured, actionable approach to lead ethical AI governance confidently.
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 3-4 hours per module, designed for flexible, self-paced learning.
If nothing changes
Without structured guidance, compliance teams risk being bypassed in AI product decisions, leading to reactive oversight, audit findings, and reputational exposure.
How this compares to the alternatives
Unlike generic AI ethics overviews, this course delivers implementation-grade tools specifically for compliance officers guiding AI product teams, structured, jurisdiction-aware, and ready for real-world application.
Frequently asked
Who is this course designed for?
Compliance, risk, and governance professionals who need to guide AI product development with precision and authority.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, a 30-day money-back guarantee is included.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced learning..
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