Transparency and Explainability in AI Systems | Free Training
November 18, 2025
This course builds directly on the foundations established in our AI Governance Foundations module and continues our structured series on AI Governance, Risk Management, and Compliance. While the first course introduced the core concepts, principles, and regulatory landscape, this course goes deeper into the essential pillars of transparency and explainability. It is designed to help you understand why these principles matter and apply them in practice, aligning with international standards such as ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act. Together, these courses form a progressive learning pathway, equipping you with the knowledge and tools to implement, monitor, and audit AI systems responsibly as you advance through the full AI GRC curriculum.
Transparency and explainability are two of the most critical principles in the governance of artificial intelligence. They provide the foundation for trust, accountability, and meaningful oversight of AI systems.
Transparency refers to making the inner workings, design choices, data sources, and limitations of an AI system visible and understandable to relevant stakeholders. It ensures that users, regulators, auditors, and impacted individuals are not left in the dark when an AI system makes or supports decisions.
Explainability, on the other hand, refers to the ability of the AI system to communicate the reasoning behind its outputs in clear, human-understandable terms. While transparency focuses on openness and disclosure, explainability focuses on comprehension and clarity.
Transparency and explainability are essential to ensure that AI systems are not “black boxes” but instead are interpretable, predictable, and accountable. This module introduces the objectives, scope, and structure of transparency and explainability, setting the stage for exploring how these principles are embedded in international standards such as ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act.
Participants will also learn about the risks associated with opaque systems, the benefits of making AI interpretable, and the organizational responsibilities in applying these principles throughout the AI lifecycle.
By the end of this module, learners should recognize transparency and explainability as mandatory elements for building trustworthy AI systems.
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