AIGP - Complete Course | Lead Responsible AI Governance

This lecture defines artificial intelligence in practical governance terms and distinguishes it from traditional automation, software logic, analytics, and rule-based decision systems. Learners will understand why definitions matter, because regulatory scope, policy obligations, risk classification, and governance controls often depend on how an AI system is defined.

This lecture explains major AI categories in simple language, including machine learning, deep learning, foundation models, generative AI, and multimodal systems. Learners will understand how different model types create different governance questions around data dependency, explainability, reliability, safety, misuse, monitoring, and accountability.

This lecture compares classic AI systems such as predictive, classification, recommendation, and decision-support models with generative AI systems that produce new text, images, code, audio, or other content. Learners will understand how each type creates different risks, controls, testing needs, and governance expectations.

This lecture clarifies the difference between an AI model, AI system, application, product, and AI-enabled business process. Learners will understand why governance cannot stop at the model level, because real-world risk often appears through integrations, user workflows, data flows, deployment context, and business decisions.

This lecture explains how AI systems are trained, tested, used, monitored, and improved over time. Learners will understand the difference between training and inference, where data-related risk appears, and why feedback loops must be governed to prevent drift, bias reinforcement, privacy exposure, or uncontrolled model behavior.

This lecture explains why many AI systems behave probabilistically rather than producing the same predictable output every time. Learners will understand how uncertainty affects validation, explainability, monitoring, accountability, user reliance, and the need for ongoing governance after deployment.

This lecture explains why AI requires governance beyond ordinary IT controls. Learners will understand how opacity, autonomy, speed, scale, data dependency, misuse potential, and societal impact create risks that require structured oversight, clear accountability, lifecycle controls, and continuous monitoring.

This lecture explores the main categories of AI harm affecting individuals, groups, organizations, and society. Learners will understand how bias, discrimination, privacy invasion, safety failure, misinformation, manipulation, reputational damage, and loss of autonomy can emerge from poorly governed AI systems.

This lecture explains fairness and bias as both technical and governance issues. Learners will understand how biased data, poor representation, proxy variables, flawed objectives, and weak review processes can produce unfair outcomes, and how governance teams can respond through assessment, testing, oversight, and escalation.

This lecture explains the difference between transparency, explainability, interpretability, disclosure, and meaningful user understanding. Learners will understand when explanations are needed, who needs them, what makes them useful, and how documentation can support trust, accountability, regulatory readiness, and responsible user reliance.

This lecture explains how AI systems introduce privacy and security risks through training data, prompts, outputs, embeddings, logs, model APIs, third-party processing, and user interactions. Learners will understand how privacy and cybersecurity controls must be embedded into AI governance rather than treated as separate afterthoughts.

This lecture explains safety, reliability, robustness, hallucination, brittleness, adversarial inputs, and model drift in governance terms. Learners will understand why AI systems require structured testing, defined performance thresholds, monitoring, escalation paths, and maintenance plans to remain trustworthy in real-world conditions.

This lecture explains why AI governance must define accountability, decision rights, escalation paths, and meaningful human involvement. Learners will understand the difference between symbolic oversight and real accountability, including how human reviewers must be competent, empowered, independent when needed, and able to challenge AI outputs.

This lecture translates responsible AI principles into operational practices that organizations can actually implement. Learners will understand how principles such as fairness, transparency, privacy, safety, accountability, and human-centered design become policies, intake questions, approval gates, controls, metrics, documentation, and evidence.

This lecture explains how to structure an AI governance program that can operate across the enterprise. Learners will understand the role of governance committees, policies, standards, risk ownership, escalation forums, reporting cadences, and integration with enterprise risk management.

This lecture explains how AI governance should enable innovation while defining boundaries for acceptable and unacceptable AI risk. Learners will understand how organizations set AI governance objectives, define prohibited and acceptable uses, establish escalation thresholds, and align AI strategy with risk appetite.

This lecture presents centralized, federated, and hybrid AI governance operating models. Learners will understand how each model fits different organizational sizes, maturity levels, regulatory pressures, and innovation needs, while recognizing the strengths and limitations of each approach.

This lecture builds a practical RACI model for AI governance across business, legal, privacy, cybersecurity, risk, compliance, data, procurement, audit, and product teams. Learners will understand how to reduce ownership gaps by clearly assigning responsibility, accountability, consultation, and informed stakeholders across the AI lifecycle.

This lecture explains how to create and maintain an AI inventory and use case register. Learners will understand what metadata to capture, including use case purpose, system owner, model type, vendor, data sources, risk rating, legal considerations, monitoring status, and approval history.

This lecture introduces the intake and triage process for proposed AI use cases. Learners will understand how to screen new ideas, identify prohibited uses, classify risk, route reviews to the right stakeholders, and create approval workflows that balance innovation speed with responsible oversight.

This lecture explains the policy architecture required for AI governance. Learners will understand how AI policies, standards, procedures, and guidelines cover acceptable use, procurement, development, data governance, monitoring, incident response, third-party requirements, and role-based responsibilities.

This lecture explains how to design an AI training and awareness program for different stakeholder groups. Learners will understand how executives, developers, business users, legal, privacy, procurement, risk, and compliance teams need different levels of AI knowledge, risk awareness, and role-specific responsibilities.

This lecture explains how to measure and report AI governance performance using meaningful metrics. Learners will understand how to track inventoried AI systems, risk assessments completed, unresolved high risks, vendor reviews, monitoring exceptions, incidents, policy exceptions, and executive-level governance trends.

This lecture explains core AI risk management concepts, including risk identification, likelihood, impact, severity, inherent risk, residual risk, treatment, acceptance, and monitoring. Learners will understand how standard risk management logic applies to AI while recognizing the unique uncertainty and harm patterns of AI systems.

This lecture builds a practical taxonomy for AI risks across legal, ethical, privacy, cybersecurity, safety, operational, financial, reputational, societal, and third-party categories. Learners will understand how a common taxonomy improves consistency across assessments, reporting, control design, and escalation decisions.

This lecture explains how to perform or review AI impact assessments. Learners will understand how to assess purpose, stakeholders, affected groups, data use, potential harms, legal triggers, human oversight, testing evidence, monitoring needs, and approval conditions before deployment.

This lecture explains how to assess AI harms using likelihood, severity, affected population, reversibility, scale, and vulnerability of impacted groups. Learners will understand how harm matrices support prioritization, defensible risk decisions, and escalation when potential impact exceeds organizational tolerance.

This lecture explains how organizations treat AI risks through avoidance, reduction, transfer, control, monitoring, and acceptance. Learners will understand how to select appropriate mitigations, recognize unacceptable residual risk, and avoid approving high-risk use cases without sufficient safeguards.

This lecture explains how to convert AI risks into practical controls across policy, process, technical testing, human oversight, logging, monitoring, vendor contracts, and incident response. Learners will understand how to write evidence-ready control descriptions that clearly connect risk scenarios to governance expectations.

This lecture explains how AI governance controls can be tested through interviews, document review, sampling, monitoring evidence, audit logs, testing reports, red team findings, and issue tracking. Learners will understand how assurance activities confirm whether controls are designed properly and operating effectively.

This lecture explains how AI risk acceptance should be documented, approved, monitored, and revisited. Learners will understand how to define accountable approvers, acceptance conditions, expiry dates, compensating controls, escalation triggers, and evidence requirements to avoid informal or hidden risk decisions.

This lecture introduces the legal landscape around AI, including AI-specific regulation, privacy law, consumer protection, discrimination, intellectual property, liability, and sector-specific rules. Learners will understand that AI legal risk is not one single regulation, but a combination of obligations shaped by use case, data, jurisdiction, and impact.

This lecture explains risk-based AI regulation concepts such as prohibited, high-risk, limited-risk, and minimal-risk systems. Learners will understand how classification affects obligations, documentation, oversight, transparency, testing, and whether certain AI uses may be restricted or unacceptable.

This lecture clarifies regulatory and governance role labels across the AI lifecycle. Learners will understand how one organization may act as a provider, developer, deployer, importer, distributor, or user depending on context, and why role classification matters for obligations and accountability.

This lecture explains common obligations associated with high-risk AI systems. Learners will understand how risk management, data governance, technical documentation, human oversight, transparency, quality management, record keeping, monitoring, and compliance evidence support defensible governance.

This lecture explains governance challenges related to general-purpose AI and foundation models. Learners will understand downstream use risk, systemic risk, model documentation, deployer reliance, limitations, transparency expectations, and why organizations must evaluate foundation models before integrating them into business processes.

This lecture discusses enforcement expectations, penalties, supervisory review, accountability evidence, and defensible governance. Learners will understand why organizations must be able to explain AI decisions, show risk-based controls, demonstrate oversight, and prove that governance was active rather than superficial.

This lecture provides a comparative overview of major AI regulatory approaches without requiring country-by-country memorization. Learners will understand common global themes such as risk classification, transparency, human oversight, privacy protection, safety, accountability, and governance documentation.

This lecture explains how privacy principles apply to AI systems, including transparency, choice, lawful basis, purpose limitation, data minimization, privacy by design, and accountability. Learners will understand how privacy impact considerations become governance requirements during design, data use, deployment, and monitoring.

This lecture explains how data subject rights apply when AI systems process personal data or support automated decisions. Learners will understand access, correction, deletion, objection, explanation, human intervention, record keeping, and the governance controls needed to respond to rights requests.

This lecture explains heightened governance expectations for sensitive data such as biometrics, health data, children’s data, financial data, and other special categories. Learners will understand why these data types require stronger controls, legal review, stricter access, enhanced documentation, and escalation before AI use.

This lecture explains how AI systems create cross-border transfer issues through cloud processing, vendor APIs, support access, global subprocessors, logs, embeddings, and model hosting. Learners will understand how governance teams assess processing flows, document transfer risk, and coordinate with legal and privacy teams.

This lecture explains intellectual property risks related to training data, copyright, generated content, licensing restrictions, confidential data, and trade secrets. Learners will understand how AI governance controls help prevent unauthorized content use, unclear ownership, accidental disclosure, and violation of contractual or licensing terms.

This lecture explains discrimination risk in AI systems used for employment, lending, credit, housing, insurance, education, and access to services. Learners will understand how proxy variables, biased datasets, opaque scoring, and poor validation can create unfair outcomes that require fairness review and governance escalation.

This lecture explains how AI can create consumer protection risk through misleading claims, dark patterns, unsafe outputs, hidden automation, deceptive design, or overreliance on AI-generated recommendations. Learners will understand how disclosures, testing, design review, and monitoring reduce the risk of unfair or deceptive practices.

This lecture explains liability concerns related to unsafe AI design, defective outputs, failure to warn, foreseeable misuse, inadequate testing, and weak post-deployment monitoring. Learners will understand how validation, documentation, transparency, and maintenance practices support defensibility when AI systems fail.

This lecture explains the OECD trustworthy AI principles and their influence on global regulation, standards, and governance practices. Learners will understand how values such as inclusive growth, human-centered design, transparency, robustness, safety, security, and accountability can be translated into organizational controls.

This lecture introduces the NIST AI Risk Management Framework and its value for structuring AI governance. Learners will understand the Govern, Map, Measure, and Manage functions and how they support risk identification, assessment, treatment, monitoring, and program-level decision-making.

This lecture explains how playbook-style questions can operationalize AI risk management. Learners will understand how structured questions improve assessments, support consistent review, guide stakeholder discussions, and help governance teams identify missing evidence, unclear accountability, and unresolved risk.

This lecture explains ISO/IEC 42001 as an AI management system standard for governance, accountability, risk management, policy control, performance evaluation, and continual improvement. Learners will understand how management system thinking helps organizations build repeatable and auditable AI governance practices.

This lecture explains why shared AI terminology is essential for governance. Learners will understand how consistent definitions reduce confusion between legal, technical, business, compliance, and audit teams, especially when discussing AI systems, models, data, risk, transparency, and accountability.

This lecture explains AI impact assessment concepts and their role in risk identification, documentation, and accountable decision-making. Learners will understand how impact assessments support approval gates, stakeholder review, harm analysis, mitigation planning, and defensible AI deployment.

This lecture explains how to combine OECD principles, NIST AI RMF, ISO standards, privacy requirements, security controls, and internal policies into one integrated governance program. Learners will understand how to reduce duplication, map controls efficiently, and build a unified governance framework.

This lecture explains how to define the AI use case before design or deployment begins. Learners will understand how purpose, intended users, business objectives, expected benefits, foreseeable harms, decision boundaries, and unintended uses shape governance requirements.

This lecture explains how governance requirements should be captured alongside functional and technical requirements. Learners will understand how compliance, data governance, fairness, explainability, human oversight, security, monitoring, and documentation expectations become design requirements rather than late-stage corrections.

This lecture explains governance considerations when selecting model type, vendor model, open-source model, proprietary model, fine-tuning, retrieval-augmented generation, or agentic architecture. Learners will understand how architecture choices affect privacy, transparency, cost, security, control ownership, and operational risk.

This lecture explains how meaningful human oversight should be designed into AI systems from the beginning. Learners will understand human-in-the-loop, human-on-the-loop, escalation, override, competence, independence, and accountability, while avoiding symbolic review that does not reduce real risk.

This lecture explains how responsible AI principles can be embedded into design workshops, architecture reviews, product decisions, and approval gates. Learners will understand how to structure responsible design reviews, document ethical decisions, and ensure design choices reflect risk, fairness, transparency, and human impact.

This lecture explains why AI governance should include impacted users, affected groups, domain experts, legal teams, operational teams, and other stakeholders before deployment. Learners will understand how stakeholder engagement improves harm identification, validates assumptions, and strengthens trust in AI decisions.

This lecture explains the development documentation needed to support accountability and auditability. Learners will understand how design records, decision logs, architecture rationale, model assumptions, risk decisions, testing plans, approval evidence, and limitations create a defensible record of responsible development.

This lecture explains foundational data governance concepts for AI, including ownership, lawful rights, data quality, quantity, representativeness, integrity, fitness for purpose, and approval controls. Learners will understand why AI governance depends heavily on the quality and legitimacy of the data behind the system.

This lecture explains how organizations assess whether data can lawfully and responsibly be used for AI. Learners will understand consent, contractual rights, legitimate use, licenses, public data, scraped data, third-party data, restricted datasets, and the importance of documenting data rights.

This lecture explains how poor, incomplete, outdated, imbalanced, or unrepresentative data can lead to poor AI outcomes and governance failures. Learners will understand how data quality review, representativeness checks, and corrective action reduce bias, reliability issues, and unfair impact.

This lecture explains how to document where data came from, how it changed, who processed it, and whether it remains suitable for training, testing, validation, or inference. Learners will understand how lineage and provenance support auditability, compliance, accountability, and incident investigation.

This lecture explains the purpose and governance importance of training, testing, and validation datasets. Learners will understand dataset separation, leakage risk, validation logic, performance testing, fairness testing, and dataset documentation required to support reliable and defensible AI outcomes.

This lecture explains data retention and deletion challenges in AI environments. Learners will understand how deletion requests, retention schedules, retraining implications, embeddings, logs, backups, and downstream dependencies create governance issues that must be addressed before and after deployment.

This lecture explains how to build a testing strategy for AI systems based on risk and intended use. Learners will understand how functional testing, performance testing, bias testing, security testing, robustness testing, integration testing, and user acceptance testing support responsible deployment decisions.

This lecture explains how to test AI systems for disparate impact, group performance differences, proxy variables, and fairness concerns. Learners will understand how fairness findings should be interpreted, escalated, mitigated, and documented before a system is approved for use.

This lecture explains how to evaluate whether AI explanations are technically valid, meaningful to users, suitable for regulators, and appropriate for the system’s risk level. Learners will understand how to document explanation limits and avoid overstating model transparency.

This lecture explains AI-specific security threats such as prompt injection, data leakage, model extraction, adversarial examples, insecure plugins, tool abuse, supply chain issues, and unauthorized access. Learners will understand how threat modeling supports security controls across architecture, deployment, and monitoring.

This lecture explains the purpose, scope, and governance value of AI red teaming and safety evaluation. Learners will understand how red team exercises test misuse, unsafe outputs, prohibited content, policy bypasses, and system weaknesses, while creating findings that must be tracked to remediation.

This lecture explains key AI documentation artifacts such as model cards, data sheets, technical files, and system documentation. Learners will understand how these artifacts communicate model purpose, limitations, data sources, performance, risk, intended use, foreseeable misuse, and monitoring expectations.

This lecture explains how controlled pilots and release readiness reviews reduce deployment risk. Learners will understand how to define acceptance criteria, performance thresholds, user feedback channels, rollback plans, approval gates, and evidence needed to support a responsible go-live decision.