Developing Explainable AI (XAI)

Instructor: Brinnae Bent, PhD

Intermediate Level • 8 hours to complete Recommended experience • Flexible Schedule

What You'll Learn

                Define key Explainable AI terminology and their relationships to each other
Describe commonly used interpretable and explainable approaches and their trade-offs
Evaluate considerations for developing XAI systems, including XAI evaluation approach, robustness, privacy, and integration with decision-making

            

Skills You'll Gain

Data Ethics
Artificial Intelligence and Machine Learning (AI/ML)
Applied Machine Learning
Machine Learning Methods
Artificial Intelligence
Generative AI
Artificial Neural Networks
Machine Learning
Information Privacy

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

                    
                                Learn new concepts from industry experts
                            
                                Gain a foundational understanding of a subject or tool
                            
                                Develop job-relevant skills with hands-on projects
                            
                                Earn a shareable career certificate

There are 3 modules in this course

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.

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.

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.