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Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

None.

Optional for Students

None.

References and background for students:

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.
Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267, 1-38.
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160.
Extra: Nauta, M., van Bree, R., & Seifert, C. (2021). Neural prototype trees for interpretable fine-grained image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14933-14943).
Extra: van der Waa, J., Nieuwburg, E., Cremers, A., & Neerincx, M. (2021). Evaluating XAI: A comparison of rule-based and example-based explanations. Artificial Intelligence, 291, 103404.

Recommended for Teachers

Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys, 16, 1-85.

Outline

Overview of current XAI approaches and their shortcomings (15mins)

Limitations of SHAP (and by extension LIME).
Limitations in terms of understandability of XAI for end users.

10 challenges posed by Rudin et al. (30mins)

Sparse logical models
Scoring systems
Generalized additive models
Modern case-based reasoning
Supervised and unsupervised disentanglement of neural networks
Dimension reduction for data visualization
Machine learning models that incorporate physics/causality
Choosing “Rashomon” set of good models
Interpretable reinforcement learning

Reflection on when not to use XAI techniques but opt for simpler models. (15 mins)

E.g., Explainable Boosting Machines (part of InterpretML package)

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

Title	Cutting Edge XAI
Duration	60
Module	B-opt
Lesson Type	Lecture
Focus	Ethical - Trustworthy AI
Topic	General Explainable AI

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Keywords

Learning Goals