Improving Algorithmic Decision–Making in the Presence of Untrustworthy Training Data

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Although data quality is of paramount importance in algorithmic decision–making, most existing methods for supervised classification use training data without ever questioning their fidelity. At the same time, counterfactual explanation approaches widely used for post–hoc explanation of algorithmic decisions may result in unrealistic recommendations when left unconstrained. This work highlights a significant research problem, and introduces a novel framework to improve supervised classification in the presence of untrustworthy data, while offering actionable suggestions when an undesirable decision has been made (e.g., loan application rejection). Evaluation results spanning datasets from different domains demonstrate the superiority of the proposed approach, and its comparative advantage as the percentage of mislabeled instances increases.