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Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
Differentially-private (DP) mechanisms can be embedded into the design of a machine learningalgorithm to protect the resulting model against privacy leakage, although this often comes with asignificant loss of accuracy.
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
We then leverage these properties to propose a novel evaluation framework which can quantitatively measure disparities in the quality of explanations output by state-of-the-art methods.
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