Abstract: Methods and systems for generating a model to be used for evaluating credit and loan applications are provided. The method includes: training a first model by using all features included in a universe of candidate features; measuring a first metric that relates to an accuracy of the first model and a second metric that relates to a disparity of the first model; constructing a graph based on pairwise correlations of the features; clustering the features into feature sets; estimating a respective disparity contribution associated with each feature set; selecting feature sets to be included in a second model; training, the second model; measuring the first metric and the second metric with respect to the second model; and determining whether the second model satisfies a predetermined accuracy level and a predetermined disparity reduction with respect to the first model.

Abstract: A method for generating a recommendation for a potential investment is provided. The method includes: receiving first information that relates to each respective company from among a plurality of companies; generating, for each respective company based on the first information, a first vector representation; receiving second information that relates to each respective investor from among a plurality of investors; generating, for each respective investor based on the second information, a second vector representation; using the first vector representations and the second vector representations as inputs to an artificial intelligence-based algorithm in order to calculate a respective similarity metric for each respective company-investor pair; generating the recommendation for the potential investment based on the calculated similarity metrics; and generating an explanation for the recommendation.

Abstract: A method for using a neural network to generate an improved graph model includes receiving, by the neural network, a graph model. The graph model is based on data relating to an environment for allocating resources to a first group and a second group. The method further includes receiving, by the neural network, a budget for editing the graph model based on a cost of corresponding modification to the environment, and determining, by the neural network, a fairness representation based on a fairness requirement between the first and second groups. It is determined by the neural network, a utility function for the graph model based on first and second group utilities representing resource allocation to the first and second groups respectively. Reinforcement learning is performed on the neural network to generate the improved graph model using the utility function and the fairness representation.