Lecture: Guest Lecture on Explainable Machine Learning (XAI)

Lecture: Guest Lecture on Explainable Machine Learning (XAI)

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

Obligatory for Students

References and background for students

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

Recommended for Teachers

None.

 

Lesson materials

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

HCAIM Consortium

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    Auteur
    HCAIM Consortium
    Laatst gewijzigd
    2024-02-14 22:34:37
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    Gebruikte Wikiwijs Arrangementen

    HCAIM Consortium. (z.d.).

    Acknowledgement

    https://maken.wikiwijs.nl/198386/Acknowledgement

    HCAIM Consortium. (z.d.).

    Lecture: Generative Models, Transform Deep Learning and Hybrid learning models

    https://maken.wikiwijs.nl/202197/Lecture__Generative_Models__Transform_Deep_Learning_and_Hybrid_learning_models

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