Abstract: Transfer knowledge from auxiliary data for more inclusive machine learning models is provided. A method can include generating a common feature space comprising first data features, wherein the first data features are present in training data used to train a first machine learning model, and wherein the first data features are present in auxiliary data that are independent of the training data; generating a combined learned feature representation, the combined learned feature representation being representative of the first data features of the common feature space and second data features that are unique to the training data; and training a second machine learning model based on the combined learned feature representation.

Abstract: A method for minimizing algorithmic bias in machine learning models includes generating, using a first machine learning model, a first output representing a prediction, where the first output is generated in response to a first plurality of inputs including sensitive features and non-sensitive features, generating, using a second machine learning model, a second output that minimizes an influence of an algorithmic bias in the first output, where the second output is generated in response to a second plurality of inputs including the non-sensitive features and the first output, and generating a recommendation related to the prediction based on the second output.

Abstract: A method includes constructing an information graph based on a set of training data provided to a machine learning algorithm, identifying an area of the information graph in which to increase an inclusion of the information graph, wherein the inclusion comprises a consideration of a population that is underrepresented in the information graph, collecting, from an auxiliary data source, auxiliary data about the population for use in increasing the inclusion of the information graph, utilizing the auxiliary data to increase the inclusion of the information graph, to generate an updated information graph, using the updated information graph to generate a test output that incorporates information from the auxiliary data, generating, when the test output satisfies an inclusion criterion, a runtime output using the updated information graph, receiving user feedback regarding the runtime output, and determining, in response to the user feedback, whether to further increase inclusion of the runtime output.

Abstract: Aspects of the subject disclosure may include an artificial intelligence method of routing customer service interactions which begins with an analysis of a body of historical interactions with customers. By monitoring an interaction between a user and an agent, the user's issue may be determined and a potential resolution resource may be identified. By monitoring an exchange between the user and the resource, the analysis may be updated. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include an artificial intelligence method of routing customer service interactions which begins with an analysis of a body of historical interactions with customers. By monitoring an interaction between a user and an agent, the user's issue may be determined and a potential resolution resource may be identified. By monitoring an exchange between the user and the resource, the analysis may be updated. Other embodiments are disclosed.