How to Improve AI Governance and Transparency

Advised Skills

Learn how to improve AI governance and transparency across your organisation. This video explains why many AI models become risky without proper documentation, monitoring, and explainability — and presents practical steps you can take to fix it. Discover how to build an AI Governance Framework, create a Model Registry, and apply Explainable AI (XAI) techniques for better trust and compliance.

Transcript

00:00AI is powerful, but without proper governance it becomes risky and unpredictable.

00:06AI models are often created quickly, without proper documentation, monitoring, or explainability.

00:12This leads to risks around bias, security, ethics, and compliance, especially with regulations like the EU AI Act.

00:20Why does this happen?

00:22Data science teams work in isolation, processes are inconsistent, and there's no standard way to validate or review models.

00:30Start by building an AI governance framework.

00:33Define clear policies for model creation, documentation, testing, approval, and lifecycle management.

00:40Next, create a model registry. Track versions, performance, training data, ownership and audit logs for every model.

00:48And implement explainable AI techniques like LIME, SHAP, and partial dependence plots.

00:53This helps non-technical stakeholders understand how your model makes decisions.

00:59Good governance is more than compliance.

01:01It increases trust and accelerates AI adoption across the organization.

01:06Advance your skills. Transform your organization.

01:09Learn more with advised skills.

01:11Learn more with advised skills.

01:12Learn more with advised skills.

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