Course Code: xai
Duration: 14 hours
Prerequisites:
  • Basic understanding of machine learning concepts
  • Familiarity with Python programming

Audience

  • AI beginners
  • Data science enthusiasts
Overview:

Explainable AI (XAI) is an essential field that focuses on making AI models more interpretable and transparent. This course introduces participants to the fundamental concepts, techniques, and real-world applications of XAI, helping them understand the importance of transparency in AI model development.

This instructor-led, live training (online or onsite) is aimed at beginner-level professionals who wish to learn the basics of Explainable AI and its role in developing responsible AI systems.

By the end of this training, participants will be able to:

  • Understand the fundamental concepts of Explainable AI.
  • Explore the importance of transparency and interpretability in AI models.
  • Learn basic techniques for making AI models explainable.
  • Apply XAI techniques to simple machine learning models.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Explainable AI

  • What is Explainable AI (XAI)?
  • Importance of transparency in AI models
  • Key challenges in AI interpretability

Basic XAI Techniques

  • Model-agnostic methods: LIME, SHAP
  • Model-specific explainability methods
  • Explaining decisions made by black-box models

Hands-On with XAI Tools

  • Introduction to open-source XAI libraries
  • Implementing XAI in simple machine learning models
  • Visualizing explanations and model behavior

Challenges in Explainability

  • Accuracy vs interpretability trade-offs
  • Limitations of current XAI methods
  • Handling bias and fairness in explainable models

Ethical Considerations in XAI

  • Understanding the ethical implications of AI transparency
  • Balancing explainability with model performance
  • Privacy and data protection concerns in XAI

Real-World Applications of XAI

  • XAI in healthcare, finance, and law enforcement
  • Regulatory requirements for explainability
  • Building trust in AI systems through transparency

Advanced XAI Concepts

  • Exploring counterfactual explanations
  • Explaining neural networks and deep learning models
  • Interpreting complex AI systems

Future Trends in Explainable AI

  • Emerging techniques in XAI research
  • Challenges and opportunities for future AI transparency
  • Impact of XAI on responsible AI development

Summary and Next Steps

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