AI Governance Professional (AIGP) Certification Masterclass

Explore the definitions and types of AI, from narrow AI to AGI, and the relationships to machine learning and deep learning, with governance implications.

Examine four AI categories—rule-based expert systems, machine learning, generative AI and large language models, plus computer vision and natural language processing—and their governance, including data handling, bias, and transparency.

Explore four ai harm dimensions—individual privacy and autonomy, group bias and exclusion, organizational liability and compliance, and societal risks like misinformation and inequality—and learn governance strategies to mitigate them.

Discover how AI governance differs from traditional software by examining four characteristics: complexity and opacity, autonomy, speed, scale, and why data quality and probabilistic outputs demand human oversight and testing.

Explore four foundational principles of responsible AI—fairness and non-discrimination, safety, security, and reliability, privacy and data protection, and transparency, explainability, and accountability—along with governance practices and risk considerations.

Drive AI governance through four layers: executive leadership and board oversight, governance committees, technical teams, and business stakeholders, ensuring responsible AI, risk management, and coordinated deployment.

Coordinate cross-functional collaboration across data science, legal, privacy, risk, security, ethics, HR, and operations to govern AI responsibly. Emphasize diverse perspectives, early involvement, structured processes, psychological safety, and lifecycle governance.

Establish a common AI vocabulary with foundational terminology and concepts. Align training with organizational AI strategy and governance policies and procedures, delivering role-specific education for responsible deployment.

Tailor AI governance to your organization's size, industry, product goals, and risk appetite, scaling rigor from startups to large enterprises while meeting sector rules and high-stakes requirements.

Understand four AI governance stakeholders—developers, deployers, users, and multi-role organizations—and their distinct, overlapping responsibilities and the need for transparent, documented governance.

Assess AI use cases through four components: business case evaluation, risk screening, stakeholder engagement, and governance gates, to decide whether to deploy AI after defining the problem and assessing feasibility.

Identify AI risks with structured risk assessment methodologies and embed ethics by design. Address impact assessment requirements and apply risk mitigation and control measures across policy areas.

Develop policies for data collection and sourcing, ensure data quality and fitness for purpose, and govern data lineage with privacy, security, and consent controls for responsible ai training.

Establish and enforce development standards, architectural standards, training and validation procedures, and testing protocols, model documentation to ensure responsible, reproducible ai across teams with auditable bias, privacy, and security controls.

Learn four operational policy areas of AI governance—deployment readiness criteria, continuous monitoring, incident response, and documentation and reporting—to ensure rigorous infrastructure, compliance, and ongoing governance throughout the lifecycle.

Evaluate existing privacy and security policies through an ai lens, identify ai-specific gaps, and implement data privacy, security, and vendor management provisions for ai training and deployment.

Explain privacy notice obligations for ai processing and enforce granular, per-purpose consent, including explicit consent for special category data, and address gdpr automated decision rules with meaningful oversight.

Define data use upfront with concrete purposes, assess compatible versus incompatible uses, and minimize AI training data through feature selection. Embed privacy by design and default from the start.

This lecture explains the data controller obligations in AI systems, including privacy impact assessments, managing third-party processors, cross-border data transfers, and maintaining living documentation with model cards and decision logs.

Navigate copyright, fair use, patents, and trade secrets in AI—from training data to generated content—across jurisdictions, with licensing, human authorship, and ownership at the center.

Understand how AI intersects civil rights laws across employment, credit, housing, and insurance, auditing for disparate impact. Ensure explainability, adverse action notices, and ongoing human oversight.

Explore how consumer protection laws govern ai, covering unfair and deceptive practices, truth in advertising, disclosure obligations, and enforcement risks to build trusted, compliant ai applications.

Explore AI product liability by analyzing design defects, manufacturing defects, and failure-to-warn, plus AI-specific liability in supply chains to build safer systems.

Explore the EU AI Act's risk framework with four levels: prohibited, high-risk, limited-risk, and minimal-risk, including Article 5 prohibitions and penalties up to 35 million euros or 7% turnover.

Implement continuous risk management, data governance, and comprehensive technical documentation to meet the EU AI Act requirements for high-risk AI, and pursue conformity assessments for CE marking.

Understand how the EU AI Act ties transparency, human oversight, and disclosure to risk levels, preserving human agency. Learn how quality management systems enforce these obligations in high-risk AI.

Define general purpose AI models under the eu ai act and contrast them with purpose-built AI. Examine governance, systemic risk obligations, and downstream provider responsibilities.

Explore the EU AI Act enforcement landscape, including substantial penalties, national authorities, and the European AI Office, while clarifying provider, deployer, importer, and distributor obligations across the AI value chain.

Explore the OECD AI principles and policy framework that guide trustworthy AI, including transparency, accountability, risk management, and their adoption by over 50 countries shaping governance and innovation.

Explore the NIST AI RMF and its four core functions—govern, map, measure, manage—and use the playbook and risk management profiles to tailor responsible AI across sectors.

Explore ARIA's measurement-centered approach to AI evaluation, with standardized tools, datasets, and benchmarks assessing safety, fairness, robustness, privacy, and transparency.

Explore ISO/IEC 22989 terminology and ISO/IEC 42001 AI governance, and learn certification steps, risk management, bias mitigation, and governance implications for organizations.

Define your ai project's business context and use case by clarifying business objectives and success criteria, problem definition and scoping, stakeholder needs, and feasibility to ensure a practical, measurable deployment.

Explore four impact assessment types for AI: AI impact assessments, data protection impact assessments, ethical impact assessments, and integrated assessments, a practical method to identify, assess, mitigate, and document impacts.

Identify jurisdictional, sector-specific, and data protection laws for your AI system (GDPR, EU AI Act) and create a compliance matrix with a gap analysis to guide remediation.

Design and development best practices guide turning requirements gathering into architecture design, model selection, and governance-aligned ai, with human oversight and data analysis plus feature engineering for fairness and privacy.

Embed ethics by design into ai development from day one, integrating fairness, accountability, and human dignity with stakeholder engagement, concrete metrics, and ongoing safeguards.

Identify and manage AI risks through a continuous framework covering internal and external risks, probability-severity matrices, a mitigation hierarchy, and stakeholder and use-case evaluations.

Explore rigorous testing and validation approaches, including benchmarking baselines, pre-deployment pilots, simulation and scenario analysis, adversarial and stress testing, and user acceptance testing to ensure usable, trustworthy deployments.

Document your AI comprehensively—from design standards and data architecture to decision logs, compliance evidence, and version control and change management for auditability.