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Explainable AI

Machine learning and artificial intelligence are very flexible and powerful methods for modelling and prediction, but they rarely provide any human understandable explanation or justification for their predictions.

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This may have severe consequences if the models are dealing with people e.g. credibility scores or medication recommendations, but even when the consequence may seem less severe, there may be a reluctance to use machine learning methods if they cannot be interpreted and validated.

We focus on the development of AI methods for obtaining insight of the physical system but taking advantage of the flexibility and precision of black box methods.

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