Data Lineage & Ethical Frameworks for Responsible AI
By External Provider
About this course
Transform how your organization builds trust in AI. Learn to design end‑to‑end data lineage and ethical governance frameworks that make AI explainable, auditable, and compliant—without slowing innovation. In this hands‑on course, you’ll capture provenance from source to model, manage metadata and documentation (datasheets, model cards), and operationalize risk controls aligned to industry guidance. Practice integrating lineage with the AI lifecycle—ingestion, training, deployment, monitoring—and implement privacy, fairness, and quality assurance guardrails. You’ll produce audit‑ready evidence packs, dashboards, and review artifacts that withstand scrutiny from leadership, regulators, and clients. By the end, you’ll design governed AI workflows, evaluate risks and mitigations, and institutionalize ethical review gates for reliable, accountable outcomes.
What you will learn
- Translate business requirements into lineage models and control points
- Capture and visualize lineage from source data to deployed models, build datasheets/model cards that make AI decisions explainable
- Apply NIST AI RMF‑aligned assessments and define mitigations
- Conduct privacy impact assessments and implement consent/retention controls & produce audit‑ready evidence packs and governance dashboards