Debiasing training data for inductive expert system construction

We study the presence of economic bias in the training data used to develop inductive expert systems. Such bias arises when an expert considers economic factors in decision making. We find that the presence of economic bias is particularly harmful when there is an economic misalignment between the expert and the user of the induced expert system. Such misalignment is referred to as differential bias. The most significant contribution of this study is a training data debiasing procedure that uses a genetic algorithm to reconstruct training data that is relatively free of economic bias. We conduct a series of simulation experiments that show: the economic performance of accuracy and value seeking algorithms is statistically the same when the training data has economic bias; both accuracy and value seeking algorithms suffer in the presence of differential bias; the proposed debiasing procedure significantly combats differential bias; and the debiasing procedure is quite robust with respect to estimation errors in its input parameters.