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