Administrative information

Title	Introduction to General Explainable AI
Duration	60 min
Module	B
Lesson Type	Lecture
Focus	Ethical - Trustworthy AI
Topic	General Explainable AI

Keywords

Explainable Artificial Intelligence,Machine Learning,Deep Learning,Interpretability,Comprehensibility,Transparency,Privacy,Fairness,Accountability,Responsible Artificial Intelligence,

Learning Goals

Understand, analyze and elaborate upon the importance of XAI in the modern world.
Differentiate between transparent and opaque machine learning models.
Categorize and discuss approaches to explainability XAI based on model scope, agnosticity, data types and explanation techniques.
Discern, investigate and discuss the trade-off between accuracy and interpretability.
Summarize and understand the working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanations.
Expand on possible applications of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets.

Expected Preparation

Learning Events to be Completed Before

None.

Obligatory for Students

Fundamentals of Python Programming
Fundamentals of Machine Learning

Optional for Students

Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning (1st ed. 2021 Edition) by Uday Kamath (Author), John Liu (Author)

References and background for students:

Recommended for Teachers

Belle V., Papantonis I., Principles and Practice of Explainable Machine Learning
Arrieta, A. B. et al., Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI
Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning (1st ed. 2021 Edition) by Uday Kamath (Author), John Liu (Author)

Lesson Materials

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

This lecture provides general insights into the field of Explainable Artificial Intelligence (XAI). Our reliance on artificial intelligence models is further discussed. Teachers can emphasize that recent laws have also caused the urgency about explaining and defending the decisions made by AI systems. This lecture discusses tools and techniques to visualize, explain, and build trustworthy AI systems.

Outline

Duration	Topic	Description
5 mins	Introduction	Definition of XAI. Why is XAI Important and What Problems Does It Solve.
5 mins	Dimensions of Explainability	What Does Explainability Mean. What Criteria Does It Have to Answer.
20 mins	Approaches to Explainability	Transparent Models and Opaque Models.
20 mins	Explainability Techniques	Approaching Explainability with Model-Specific and Model-Agnostic Techniques.
10 mins	Closing Remarks	Discussion with Students. Questions and Answers.

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities