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Course Outline

Introduction to Explainable AI

  • What constitutes Explainable AI (XAI)?
  • The critical role of transparency in AI models.
  • Principal challenges in AI interpretability.

Fundamental XAI Techniques

  • Model-agnostic methods: LIME, SHAP.
  • Explainability approaches specific to model types.
  • Providing explanations for decisions made by black-box models.

Practical Application with XAI Tools

  • Overview of open-source XAI libraries.
  • Integrating XAI into simple machine learning models.
  • Visualising explanations and model behaviour.

Challenges in Explainability

  • Trade-offs between accuracy and interpretability.
  • Limitations inherent in current XAI methods.
  • Addressing bias and fairness within explainable models.

Ethical Considerations in XAI

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

Real-World Applications of XAI

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

Advanced XAI Concepts

  • Investigating 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.
  • The impact of XAI on responsible AI development.

Summary and Next Steps

Requirements

  • A foundational understanding of machine learning concepts.
  • Familiarity with Python programming.

Target Audience

  • Beginners in the field of AI.
  • Data science enthusiasts.
 14 Hours

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