Transparency and Explainability in Data

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Transparency and Explainability in Data

Explore key aspects of building trust in data practices and AI models
Implement techniques for developing AI models that are explainable and interpretable
Examine existing ethical guidelines and frameworks that promote transparency and explainability and how this can be embedded in your organisational context

Overview

Off the shelf (OTS)

This three-part course will illuminate the multifaceted world of transparency and explainability within data and AI systems. We will begin by exploring the transparency spectrum, examining what transparency truly means, and understanding its benefits and challenges. In the second section, we will delve into the various approaches organisations can employ to enhance transparency in their data and AI systems. From technical transparency to process transparency and outcome transparency, we will uncover the strategies that empower stakeholders to gain a deeper understanding of these complex systems. Lastly, we will venture into the realm of explainable data and AI (XAI). As AI systems continue to make impactful decisions, it's imperative that we can interpret and trust their processes. This section will introduce the benefits of explainability, explore techniques to make data and AI explainable, and address the current challenges and approaches in this critical area.

By the end of this course, you will not only appreciate the significance of transparency and explainability in data and AI systems but also possess the knowledge to navigate and advocate for these essential principles more widely.