Towards a Quantitative Evaluation Metric for XAI
Finding a quantitative evaluation metric that can be used to estimate the usefulness of ML model explanations. That wil be the goal of your research in this thesis.
- Explainable AI
- Artificial Intelligence
- Machine Learning
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
As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountabilityis of utmostimportance. Explanations sit at the core of these desirable attributes of a ML system. The emerging ﬁeld is frequently called “Explainable AI (XAI)” or “Explainable ML.” The goal of explainable ML is to intuitively ex-plain the predictions of a ML system, while adhering to the needs to various stakeholders. Many explanation techniques were developed with contributions from both academia and industry. However, there are several existing challenges that have not garnered enough interest and serve as roadblocks to widespread adoption of explainable ML.
It is difficult to determine which eXplainable AI technique (XAI) is most useful in a given scenario. A proper quantitative evaluation metric to determine this does not exist at the moment. As a result, it is unknown whether the explanations created for a certain model are sufficient and usable.
The goal of your research is to find a quantitative evaluation metric that can be used to estimate the usefulness of ML model explanations. This should be a stable metric that could even be utilized in an automated MLOps flow, to give the best possible set of explanations for a given ML model. Your results will assist us in implementing XAI solutions at our clients.
About Info Support Research Center
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