BIAS DETECTION AND EXPLAINABILITY OF DEEP LEARNING MODELS

摘要

System and method for latent bias detection by artificial intelligence modeling of human decision making using time series prediction data and events data of survey participants along with personal characteristics data for the participants. A deep Bayesian model solves for a bias distribution that fits a modeled prediction distribution of time series event data and personal characteristics data to a prediction probability distribution derived by a recurrent neural network. Sets of group bias clusters are evaluated for key features of related personal characteristics. Causal graphs are defined from dependency graphs of the key features. Bias explainability is inferred by perturbation in the deep Bayesian model of a subset of features from the causal graph, determining which causal relationships are most sensitive to alter group membership of participants.