Testing the Explanations of Explainable AI
Create an AutoML-like solution that picks the most interpretable model from a range of possible good enough models.
- Artificial Intelligence
- Explainable AI
What do you get
- A challenging assignment within a practical environment
- € 1000 compensation, € 500 + lease car or € 600 + living space
- Professional guidance
- Courses aimed at your graduation period
- Support from our academic Research center at your disposal
- Two vacation days per month
What you will do
- 65% Research
- 10% Analyze, design, realize
- 25% Documentation
Explainable machine learning and artificial intelligence models have been used to justify a model’s decision-making process. This added transparency aims to help improve user performance and understanding of the underlying model. However, in practice, explainable systems face many open questions and challenges.
Model agnostic explanations are more approximations. In general there are two kinds of explanations: global and local explanations. For the latter, there’s the possibility that the explanation is off, because it only looks at the local inference. It’s possible that for opposite inference, we get exactly the same explanations. To get proper explanations we’ll need to be able to test them. Are the explanations sensible to business experts and aren’t there any same explanations for opposite outcomes? And do we know when explanations will change?
About Info Support Research Center
We anticipate on upcoming and future challenges and ensures our engineers develop cutting-edge solutions based on the latest scientific insights. Our research community proactively tackles emerging technologies. We do this in cooperation with renowned scientists, making sure that research teams are positioned and embedded throughout our organisation and our community, so that their insights are directly applied to our business. We truly believe in sharing knowledge, so we want to do this without any restrictions.
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