  Decisions in organizations are about evaluating alternatives and choosing the
one that would best serve organizational goals. To the extent that the
evaluation of alternatives could be formulated as a predictive task with
appropriate metrics, machine learning algorithms are increasingly being used to
improve the efficiency of the process. Explanations help to facilitate
communication between the algorithm and the human decision-maker, making it
easier for the latter to interpret and make decisions on the basis of
predictions by the former. Feature-based explanations' semantics of causal
models, however, induce leakage from the decision-maker's prior beliefs. Our
findings from a field experiment demonstrate empirically how this leads to
confirmation bias and disparate impact on the decision-maker's confidence in
the predictions. Such differences can lead to sub-optimal and biased decision
outcomes.
