Proceedings of the 3rd Innovations in Theoretical Computer Science Conference

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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations (2017, background): “, 2016] or by constraining models to avoid it [Dwork et al., 2012; Zafar et al., 2016]).”
Definitions, methods, and applications in interpretable machine learning (2019, background): “medicine (1), policy-making (2), and science (3, 4), as well as in auditing the predictions themselves in response to issues such as regulatory pressure (5) and fairness (6).”