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Equalizing Recourse across Groups
We measure recourse as the distance of an individual from the decision boundary of a classifier.
Assessing the Local Interpretability of Machine Learning Models
Through a user study with 1, 000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and "what if" local explainability on models that are typically considered locally interpretable.
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