Abstract: A device may receive sensitive attribute data, model prediction data, and true target data associated with a regression machine learning model, and may determine quantities of Gaussian components for Gaussian mixtures. The device may generate Gaussian mixtures of the quantities of Gaussian components based on the sensitive attribute data, the model prediction data, and the true target data, and may determine parameters of estimates of conditional densities by the Gaussian mixtures based on the sensitive attribute data, the model prediction data, and the true target data. The device may calculate an independence measure, a separation measure, and a sufficiency measure of the regression machine learning model based on the Gaussian mixtures and the parameters of the estimates of the conditional densities. The device may perform actions based on one or more of the independence measure, the separation measure, or the sufficiency measure.

Abstract: The present teaching relates to adaptive generative AI via feedback. Human evaluators evaluate an answer automatically generated by a machine expert in response to a question based on a reference from a source. The evaluation is relied on to update a fidelity metric for each human evaluator. A cumulative ranking of the answer is determined using the evaluation and the updated fidelity metric of each human evaluator. A fidelity attribute for the machine expert is updated based on the cumulative ranking. Feedback is created based on the answer, the question, the cumulative ranking, and the updated fidelity attribute for adapting the performance of the Q&A system.

Abstract: The present teaching relates to a Q&A framework for quality controlling of automatically generated answers via AI. Based on a question on a subject matter received from a user, at least one machine expert is selected to answer the question based on past performances of multiple machine experts for generating respective candidate answers to the question. Each selected machine expert creates a candidate answer based on a reference from a source. Quality assessment is performed with respect to each candidate answer from a respective machine expert and is relied on to determine a candidate answer as the answer to the question. Such determined answer is provided to the user as a response to the question.

Abstract: A device may receive protected attribute data, observation data, and target variable data associated with a machine learning model, and may include intersectional groups in the protected attribute data to expand a quantity of demographic subgroups and to generate modified protected attribute data. The device may calculate an expected proportion of individuals with the modified protected attribute data being in a particular group and the target variable data being positive, and may calculate an observed proportion of individuals with the modified protected attribute data being in the particular group and the target variable data being positive. The device may determine observation weights based on the expected proportion and the observed proportion, and may utilize the observation data and the observation weights to train the machine learning model and generate a trained machine learning model.

Abstract: A device may receive sensitive attribute data, model prediction data, and true target data associated with a regression machine learning model, and may determine quantities of Gaussian components for Gaussian mixtures. The device may generate Gaussian mixtures of the quantities of Gaussian components based on the sensitive attribute data, the model prediction data, and the true target data, and may determine parameters of estimates of conditional densities by the Gaussian mixtures based on the sensitive attribute data, the model prediction data, and the true target data. The device may calculate an independence measure, a separation measure, and a sufficiency measure of the regression machine learning model based on the Gaussian mixtures and the parameters of the estimates of the conditional densities. The device may perform actions based on one or more of the independence measure, the separation measure, or the sufficiency measure.

Abstract: Assuming that an initial social network is unavailable because explicit connections between users are missing or incomplete, temporal analysis may be used to identify the implicit relationship between social media users. Temporal data may be used to extract implicit relationship regardless of their specific activities such as visiting the same web pages or commenting on the same web objects.