Administrative information

Title	Guest Lecture on Explainable Machine Learning
Duration	60
Module	C
Lesson Type	Lecture
Focus	Technical - Future AI
Topic	Open Problems and Challenges

Keywords

Explainable AI,Interpretable models,Black Box models,Post-hoc,

Learning Goals

Understand the importance of XAI
Understand the difference between interpretable and explainable approaches
Identify XAI stakeholders
Understand the taxomony of XAI approaches
Be able to indentifiy XAI limitations and the future direction of XAI

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

Ethical Guidelines trustworthy AI

Optional for Students

How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks

References and background for students

MIT 6.S191: Robust and Trustworthy Deep Learning - YouTube video

Recommended for Teachers

None.

Lesson materials

Lecture Slides

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

Instructions for Teachers

This guest lecture should explain the importance of explainable AI and the benefits it brings to AI systems. The lecture should identify the stakeholders involved and how XAI can be beneficial for each group (engineers, end users, legislators). A distinction should be drawn between interpretable AI and explainable AI along with a discussion of why XAI may not be good enough in the long run. The lecture should identify the desired characteristics of explainability and should outline a taxonomy of XAI approaches. The lecture should end with a discussion of the limitations of exisitng XAI approaches and their future directions.

Outline

CeADAR tech talk: Towards Robust and Trustworthy AI
Duration	Description
10 min	Importance of Explainable AI (XAI)
10 min	Interpretable and Explainable AI
10 min	XAI Stakeholders
10 min	Taxonomy of XAI approaches
10 min	XAI Limitiations and Future Direction

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