Developing Explainable AI (XAI)

Duke University via Coursera

Coursera based on 22 ratings

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Overview

"Developing Explainable AI (XAI)" is a specialized course designed for AI developers, data scientists, and machine learning practitioners interested in creating transparent, interpretable, and ethical AI models. As AI systems are increasingly used in decision-making across industries, the ability to explain and understand how models make decisions is crucial for building trust and ensuring accountability. In this course, participants will explore the core principles of explainable AI (XAI), including the challenges of model interpretability, transparency, and bias. Learners will gain hands-on...

Syllabus

Responsible AI

In this module, you will be introduced to the concept of Explainable AI and how to develop XAI systems. You will learn how to differentiate between interpretability, explainability, and transparency in the context of AI; how to identify algorithmic bias, and how to critically examine ethical considerations in the context of responsible AI. You will apply these learnings through discussions and a quiz assessment.

Explainable AI Overview

In this module, you will learn how to describe XAI techniques and approaches, examine the trade-offs and challenges in developing XAI systems, and understand emerging trends in applying XAI to Generative AI applications. You will apply these learnings through discussions and a quiz assessment.

Developing XAI Systems

In this module, you will learn how to integrate XAI explanations into decision-making processes, understand considerations for the evaluation of XAI systems, and identify ways to ensure robustness and privacy in XAI systems. You will apply these learnings through case studies, discussion, and a quiz assessment.