A system for adversarial targeting mitigation is provided, the system generally comprising identifying, using an artificial intelligence and machine learning model engine, a user targeting pattern employed by an entity based on interaction data between the entity and one or more users, based on the identified pattern of targeting, training the machine learning model to identify specific user profile data correlated with specific responses from the entity, identifying, using the machine learning model, a subset of one or more favorable responses from the specific responses, and triggering the one or more favorable responses by altering the user profile data for the one or more users prior to interaction with the specific entity.

A system for adversarial targeting mitigation is provided, the system generally comprising identifying, using an artificial intelligence and machine learning model engine, a user targeting pattern employed by an entity based on interaction data between the entity and one or more users, based on the identified pattern of targeting, training the machine learning model to identify specific user profile data correlated with specific responses from the entity, identifying, using the machine learning model, a subset of one or more favorable responses from the specific responses, and triggering the one or more favorable responses by altering the user profile data for the one or more users prior to interaction with the specific entity.

Certain embodiments involve techniques for automatically identifying sounds in an audio recording that match a selected sound. An audio search and editing system receives the audio recording and preprocesses the audio recording into audio portions. The audio portions are provided as a query to the neural network that includes a trained embedding model used to analyze the audio portions in view of the selected sound to estimate feature vectors. The audio search and editing system compares the feature vectors for the audio portions against the feature vector for the selected sound and the feature vector for the negative samples to generate an audio score that is a numerical representation of the level of similarity between the audio portion and the selected sound and uses the audio scores to classify the audio portions into a first class of matching sounds and a second class of non-matching sounds.

A method and system for a feature subset-classifier pair for a classification task. The classification task corresponds to automatically classifying data associated with a subject(s) or object(s) of interest into an appropriate class based on a feature subset selected among a plurality of features extracted from the data and a classifier selected from a set of classifier types. The method proposed includes simultaneously determining the feature subset-classifier pair based on a relax-greedy {feature subset, classifier} approach utilizing sub-greedy search process based on a patience function, wherein the feature subset-classifier pair provides an optimal combination for more accurate classification. The automatic joint selection is time efficient solution, effectively speeding up the classification task.

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

An example system includes a processor that can receive conversation logs of a dialogue system to be analyzed. The processor can train a predictive machine learning model using a training set of the conversation logs on a selected feature to obtain feature values with associated importance values. The processor can select a number of feature values using a significance score calculated based on the associated importance values. The processor can generate an interactive user interface including the selected number of feature values.

An example system includes a processor that can receive conversation logs of a dialogue system to be analyzed. The processor can train a predictive machine learning model using a training set of the conversation logs on a selected feature to obtain feature values with associated importance values. The processor can select a number of feature values using a significance score calculated based on the associated importance values. The processor can generate an interactive user interface including the selected number of feature values.

Systems and methods for facilitating candidate acquisition are disclosed. The systems may include a candidate acquisition orchestration engine. The engine may include a candidate engagement optimizer, which may receive, from a database storing profiles attributes of a plurality of candidates, an expanded dataset having one or more filtered attributes pertaining to a set of candidates from the plurality of candidates. The optimizer may receive, from an entity intending to engage at least one candidate, inputs associated with preferred parameters for the at least one candidate. The optimizer may process the expanded dataset and the entity inputs through a plurality of classifiers to generate candidate predictions. A bias identification engine may optimize the candidate predictions to remove inherent bias therein so as to generate, for each classifier, optimized candidate predictions. A stack classifier may process the optimized candidate predictions to generate final candidate predictions.

Systems and methods for facilitating candidate acquisition are disclosed. The systems may include a candidate acquisition orchestration engine. The engine may include a candidate engagement optimizer, which may receive, from a database storing profiles attributes of a plurality of candidates, an expanded dataset having one or more filtered attributes pertaining to a set of candidates from the plurality of candidates. The optimizer may receive, from an entity intending to engage at least one candidate, inputs associated with preferred parameters for the at least one candidate. The optimizer may process the expanded dataset and the entity inputs through a plurality of classifiers to generate candidate predictions. A bias identification engine may optimize the candidate predictions to remove inherent bias therein so as to generate, for each classifier, optimized candidate predictions. A stack classifier may process the optimized candidate predictions to generate final candidate predictions.

A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.