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Massimiliano Mattetti

2 accepted papers

2021

User Driven Model Adjustment via Boolean Rule Explanations

AAAI 2021technical

AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic. The advances in explainability have opened the possibility of allowing users to int…

2021

What Changed? Interpretable Model Comparison

IJCAI 2021poster

We consider the problem of distinguishing two machine learning (ML) models built for the same task in a human-interpretable way. As models can fail or succeed in different ways, classical accuracy metrics may mask crucial qualitative differences. This problem arises in a few contexts. In business ap…