Responsible AI

Get started with Generative AI Data Ethics: review prerequisites, tools, environment setup, and meet your instructor for this course.

Explore why responsible AI frameworks are needed, how they guide risk management and decision-making, and examine real examples for building ethical, accountable AI systems.

Learn to spot and evaluate subtle bias in generative AI models by analyzing patterns in tone, framing, and outputs, and using systematic, evidence-based review methods for fairness.

Learn to identify, measure, and reduce bias in generative AI outputs using counterfactual testing, bias signal metrics, and post-processing mitigation techniques for fairer models.

Explore generative AI explainability by focusing on system behavior, input visibility, controlled variation, and analyzing patterns for oversight and responsible deployment.

Learn to interpret generative model outputs using XAI principles, test prompt sensitivity, apply quantitative analysis, and document behavioral risks for trustworthy, auditable AI deployment.

Learn to identify legal and ethical risks in AI data use, address copyright, fair use, licensing, data lineage, and use guardrails for responsible and compliant AI development.

Learn to audit AI training data by tracing its provenance, interpreting licenses, identifying risks, and ensuring ethical, legal, and compliant data use in generative AI projects.

Explore AI's societal and economic impacts, from changing work to trust in information and responsible design at scale.

Learn to analyze AI ethical audits, prioritize and mitigate risks, and create actionable, accountable plans to ensure safer, more responsible AI systems.

Learn how effective AI system design impacts environmental sustainability, shifting focus from training to inference, with shared responsibility and principles of efficient, Green AI development.

Learn to optimize generative AI systems by analyzing tradeoffs in cost, efficiency, and quality. Apply data-driven methods for practical, scalable, and sustainable deployments.

Learn how generative AI systems leak data via oversharing, context bleed, and prompt injection, and explore strategies like guardrails to prevent unauthorized exposure of sensitive information.

Learn to build a layered AI defense against data leaks using automated guardrails, human-in-the-loop review, and incident response plans for responsible, secure AI deployment.

Learn the essentials of algorithmic auditing: structured evidence-based evaluation of AI behavior for risk, fairness, and governance using checklists, scenarios, and repeatable audit processes.

Learn how to systematically audit generative AI models for ethical risks by detecting sensitive data leaks, scoring outputs, reviewing risks, and preparing defensible audit artifacts.

Explore human-in-the-loop systems: integrate human oversight at critical decision points in AI to ensure accountability, mitigate risks, and build responsible, trustworthy automation.

Learn to design Human-in-the-Loop (HITL) AI systems by integrating human oversight, risk-based routing, feedback loops, and workflows for safer, accountable, and effective AI content generation.

Get introduced to AI governance for generative models, course outcomes, prerequisites, and tooling to prepare for applied hands-on learning and a real-world capstone project.

Learn how data governance for Generative AI demands new frameworks, skills, and dynamic, cross-functional risk management beyond traditional approaches.

Learn to build AI governance foundations: draft a charter, map stakeholders, design a communication plan, and structure committee roles with FATE-aligned oversight for GenAI systems.

Explore global AI regulations, EU AI Act risk tiers, and how to use standards like ISO/IEC 42001 and NIST AI RMF to build scalable, cross-border AI compliance programs.

Learn to operationalize AI regulatory compliance by building a compliance matrix and Python risk classifier, mapping system risks to EU AI Act requirements, evidence, and remediation actions.

Learn to assess and manage AI risks using ISO 31000 and NIST AI RMF, focusing on technical, ethical, legal, and operational risks, prioritization, and real-world mitigation techniques.

Learn to conduct AI risk assessments by building risk registers, scoring and visualizing risks, and generating executive-ready reports using real healthcare and fintech scenarios.

Explore ISO/IEC 42001 and how it enables organizations to structure, evaluate, and certify responsible AI governance using a robust, integrated management system.

Learn to implement ISO/IEC 42001 controls by conducting gap analysis, automating compliance reporting, and using visualizations for effective AI governance and certification readiness.

Learn comprehensive AI model governance, including lifecycle stages, registries, risk tiering, documentation standards, approval workflows, and why governance is essential for GenAI systems.

Build automated AI model governance tools: generate Model Cards, track lifecycle, check approval gates, and create dashboards for scalable, auditable, and repeatable compliance.

Explore AI's data retention paradox, deletion challenges post-training, regulatory conflicts, key data types, and privacy-preserving techniques for responsible AI governance.

Learn to create automated, auditable data retention and deletion workflows, select proper deletion methods, assess model retraining needs, and ensure regulatory compliance.

Learn to identify, categorize, and mitigate the unique risks of third-party AI vendors, including data, bias, drift, lock-in, and security with robust governance and contracts.

Learn to assess and govern third-party AI vendors using risk scoring, SLA compliance dashboards, visual comparisons, and actionable recommendation tools for robust governance decisions.

Learn to design effective AI governance frameworks using five key pillars, choose the right operating model, and engage stakeholders for legitimacy and adoption.

Learn to build structured stakeholder engagement plans, impact assessments, advisory board charters, and feedback mechanisms to operationalize AI governance for vulnerable populations.

Understand unique AI incident types, detection strategies, response frameworks, severity levels, blameless learning, and emerging regulatory requirements for effective AI incident management.

Learn to operationalize AI incident response: build playbooks, severity matrices, detection and escalation logic, and reporting workflows for safety-critical AI systems.

Explore AI governance organizational design: compare governance models, use RACI for roles, establish effective ethics boards, define clear policies, and ensure accountability and escalation.

Learn to design and implement comprehensive AI governance: create enforceable use policies, RACI matrices, ethics board charters, and robust review workflows for regulatory compliance.

Understand sovereign AI: how organizations govern AI systems using FATE principles, a tiering framework, and practices like Regulatory as Code and deep literacy to align with their values and laws.

Get oriented to GenAI data management: course overview, learning goals, prerequisites, Azure setup, tooling, workspaces, and essential skills for the capstone project.

Learn how generative AI systems manage data across the entire lifecycle—collection to inference—requiring continuous governance, user data ownership, and bias monitoring beyond training.

Explore enterprise RAG: its layered data architecture, chunking strategies, retrieval design, embeddings, and the governance of prompts and conversational memory.

Learn to refactor an enterprise RAG pipeline for privacy, separating user and enterprise data, supporting per-session storage, and enabling right-to-erasure on user data.

Learn to build pipelines that process and align text and images, ensuring accurate retrieval from multimodal documents using modality-specific preprocessing and structured records.

Build a multimodal data pipeline to extract, describe, and index every image from a PDF, enabling fine-grained product retrieval in an apparel catalog RAG chatbot.

Learn why and how to version data, code, and pipelines in generative AI to enable traceability, reproducibility, auditability, and responsible AI governance.

Learn to version data, prompts, and artifacts in a RAG pipeline using DVC and Git, enabling full lineage tracing, reproducible rollbacks, and defensible audit trails in generative systems.

Explore essential access control for AI systems: authentication, authorisation, pre-retrieval filtering, identity propagation, auditability, and enforcing security throughout data pipelines.

Learn to enforce access management in AI data systems by configuring role-based policies, controlling data access pre-retrieval, and maintaining auditable query logs.

Learn how data catalogs and metadata ensure generative AI uses approved, reliable, and governed data, preventing unfiltered or unauthorized information from influencing AI outputs.

Automate data catalog classification to control, filter, and trace RAG chatbot sources by metadata, enabling category-based exports and defensible, audit-ready retrieval.

Learn what synthetic data is, when to use it, key risks like bias amplification, quality validation steps, and best practices for governance and provenance in generative AI workflows.

Build a synthetic Q&A pipeline that redacts PII before LLM use, ensuring privacy and auditability while generating clean, reviewable datasets for fine-tuning.