Administrative information
| Title |
Explainable AI for end-users |
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| Duration |
60 mins |
| Module |
B-opt |
| Lesson Type |
Lecture |
| Focus |
Ethical - Trustworthy AI |
| Topic |
General Explainable AI |
Keywords
Explainable AI,
Learning Goals
- Get a deeper understanding of how XAI can be communicated to end users
Expected Preparation
Learning Events to be Completed Before
None.
Obligatory for Students
- Dalex, a package combining various XAI techniques, and offering a tutorial
- Analysis of SHAP for visual images Bento, V., Kohler, M., Diaz, P., Mendoza, L., & Pacheco, M. A. (2021). Improving deep learning performance by using Explainable Artificial Intelligence (XAI) approaches. Discover Artificial Intelligence, 1(1), 1-11.
- Attacking discrimination in ML
- Visual intro to ML
Optional for Students
References and background for students:
None.
Recommended for Teachers
None.
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
Practical laboratory that provides learners with a well understood data set and requires them to apply a machine learning algorithm for classification. Learners will then have to select a post-hoc explainability technique (ICE, DeepLIFT, LIME, SHAP).
Students will have to apply a relevant Explainable AI technique (or methodology) on their specific project/model, and report why their approach is suited and which (ethical) problems it addresses.
Trustworthy AI is a wider concept that just applying (post hoc) XAI techniques. For Trustworthy AI, the primary model should properly be understood (before resorting to XAI tools) (non post-hoc) approach of "explainability" is also be to visualize (explain) the effect of model thresholds on e.g. fairness.
[[Review status::I think this LE can be deleted. There is a lot of overlap with LE156/LE157 (the practicals) and the other two lectures on this topic, LE154, LE158). [OurenKuiper]
This LE is a "lecture", but the description talks about a " practical laboratory"...
!!! Many important fields are empty (e.g. goals, keywords, lesson material, keywords); outline is missing!!!| ]]
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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