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Decisions in organizations are about evaluating alternatives and choosing theone that would best serve organizational goals. To the extent that theevaluation of alternatives could be formulated as a predictive task withappropriate metrics, machine learning algorithms are increasingly being used toimprove the efficiency of the process. Explanations help to facilitatecommunication between the algorithm and the human decision-maker, making iteasier for the latter to interpret and make decisions on the basis ofpredictions by the former. Feature-based explanations' semantics of causalmodels, however, induce leakage from the decision-maker's prior beliefs. Ourfindings from a field experiment demonstrate empirically how this leads toconfirmation bias and disparate impact on the decision-maker's confidence inthe predictions. Such differences can lead to sub-optimal and biased decisionoutcomes.

Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making