Abstract: A method for inferring intent and discrepancies in a label coding scheme is described. The method includes compiling data indicating how one or more individuals labeled unstructured content according to the label coding scheme comprising a plurality of labels. The method also includes analyzing a context associated with a content labeled in a particular manner by the one or more individuals. The method further includes detecting discrepancies of meaning for a particular label used by the one or more individuals. The method also includes inferring a strategic thinking of the one or more individuals associated with the discrepancies of meaning detected for the particular label. The method further includes displaying recorded metadata associated with the strategic thinking and the discrepancies of meaning detected for the particular label between the one or more individuals regarding a coded dataset.

Abstract: Systems and methods for generating predicted preferences are disclosed. The method includes implementing, with a computing device having a processor and a non-transitory computer-readable memory, a conjoint architecture comprising: an autoencoder trained to transform input data including one or more choices and one or more features into a latent representation, and a choice classification network trained to predict one or more predicted preferences from the latent representation extracted by the autoencoder. The method further includes outputting, from the choice classification network, the one or more predicted preferences.

Abstract: A method for information overweight detection and intervention is described. The method includes training a statistical model to classify salient information that may be overweight by individuals to provide a trained statistical model. The method also includes collecting data from a user about the salient information experienced by the user or to which the user is exposed. The method further includes analyzing the salient information using the trained statistical model to identify and classify the salient information that the user may overweight to identify overweight information. The method also includes presenting one or more interventions to the user to prevent the user from overweighting the identified overweight information.

Abstract: Systems and methods described herein relate to predicting the effect of an intervention via machine learning. One embodiment divides a plurality of units into first and second intervention groups that receive first and second interventions, respectively; identifies, for each unit, k nearest-neighbor units in each of the first and second intervention groups; calculates, for each unit, an outcome under the first and second interventions as first and second weighted averages of the k nearest-neighbor units in the first and second intervention groups, respectively; calculates, for each unit, an intervention effect for that unit as the difference between the outcomes under the first and second interventions; generates a machine-learning-based regression model that models the intervention effects of the units as a function of a set of covariates; and outputs, using the machine-learning-based regression model, a predicted intervention effect for a unit that is outside the plurality of units.

Abstract: A method includes fitting a ML trained model to data features, the fitting generates complete data feature-set outputs that are associated with a first set of accuracy values, iteratively fitting, after an iterative removal of each data feature from the data feature-set, the ML trained model to subsets of the plurality of data features to determine respective reduced feature-set outputs, each subset lacks a different data feature of the plurality of data features, determining one or more of the reduced feature-set outputs as corresponding to a second set of accuracy values, designating the iteratively removed data features as accuracy-modifying data features, generating a first linear model, generating a second linear model based on one of the accuracy-modifying data features having a weight that is highest relative to respective different weights of the remaining ones of the accuracy-modifying data features, and identifying the second linear model as a generative model.