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Machine learning systems have become increasingly popular in crucial high-stakes fields, such as healthcare and finance. To be effective in these domains, the models must not only make precise predictions but also provide relevant explanations for those predictions. To achieve this goal, there has been a substantial research effort in recent years to develop techniques that explain black-box models and create models that are interpretable by design.