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Coursera

Data Lineage & Ethical Frameworks for Responsible AI

Fractal Analytics via Coursera

Overview

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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.

Syllabus

  • Governance Foundations and Content Authenticity
    • Explore the core principles of data governance and ethical AI. Learn how frameworks like C2PA and tools like DuckLake help verify content authenticity and detect misinformation. Understand global governance perspectives and how to embed trust into AI systems.
  • Data Pipelines and Lineage
    • Dive into the architecture of data pipelines and learn how to build them with governance in mind. Use tools like OpenLineage to visualize, monitor, and trace data across its lifecycle—ensuring transparency and compliance.
  • Responsible AI & Applied Governance
    • Design AI systems that are both powerful and principled. Learn ethics-by-design workflows, governance automation, and advanced compliance strategies to future-proof your AI initiatives.

Taught by

David Drummond and Fractal Analytics Academy

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