METHOD AND DEVICE FOR DE-PREJUDICING ARTIFICIAL INTELLIGENCE BASED ANOMALY DETECTION

A method for de-prejudicing Artificial Intelligence (AI) based anomaly detection is disclosed. The method includes training and testing an AI model based on a labelled training data, determining whether the AI model reveals a bias, based on one or more prejudicing variables, and thereafter re-building the AI model based on iterative process of de-prejudicing the feature set of the AI model and de-prejudicing the training data. A check is made to determine whether the feature set of the AI model feature set includes any proxy variables associated with any of the prejudicing variables and identifies the weight to be assigned to a proxy variable based on the intra-cohort variation in separate machine learning models built for each cohort associated with each value of the prejudicing variable. The feature set of the AI model is de-prejudiced based on the explanatory power of the proxy variables independent of the prejudicing variables.