Generative AI Data Ethics

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.