  Machine learning (ML) models have been quite successful in predicting
outcomes in many applications. However, in some cases, domain experts might
have a judgment about the expected outcome that might conflict with the
prediction of ML models. One main reason for this is that the training data
might not be totally representative of the population. In this paper, we
present a novel framework that aims at leveraging experts' judgment to mitigate
the conflict. The underlying idea behind our framework is that we first
determine, using a generative adversarial network, the degree of representation
of an unlabeled data point in the training data. Then, based on such degree, we
correct the \textcolor{black}{machine learning} model's prediction by
incorporating the experts' judgment into it, where the higher that
aforementioned degree of representation, the less the weight we put on the
expert intuition that we add to our corrected output, and vice-versa. We
perform multiple numerical experiments on synthetic data as well as two
real-world case studies (one from the IT services industry and the other from
the financial industry). All results show the effectiveness of our framework;
it yields much higher closeness to the experts' judgment with minimal sacrifice
in the prediction accuracy, when compared to multiple baseline methods. We also
develop a new evaluation metric that combines prediction accuracy with the
closeness to experts' judgment. Our framework yields statistically significant
results when evaluated on that metric.

Machine Learning;Artificial Intelligence