Abstract: 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.

Abstract: Embodiment of the present disclosure discloses method and device for obtaining at least one influencing causal factor based on expert subjective judgement. Initially, first relative weightage values of plurality of predefined causal factors associated with each of one or more decision trees of classification in decision-making model is determined. Decision space is determined for classification based on first relative weightage values and base hypothesis associated with classification. The decision space is mapped with expert subjective judgment provided by expert user for classification. Error value associated with classification is determined based on mapping. Base hypothesis of classification is optimized such that optimized base hypothesis corresponds to minimum value of error value. Influencing causal factor from plurality of predefined causal factors are obtained by performing ensemble technique using optimized base hypothesis.

Abstract: 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.

Abstract: Embodiment of the present disclosure discloses method and device for obtaining at least one influencing causal factor based on expert subjective judgement. Initially, first relative weightage values of plurality of predefined causal factors associated with each of one or more decision trees of classification in decision-making model is determined. Decision space is determined for classification based on first relative weightage values and base hypothesis associated with classification. The decision space is mapped with expert subjective judgment provided by expert user for classification. Error value associated with classification is determined based on mapping. Base hypothesis of classification is optimized such that optimized base hypothesis corresponds to minimum value of error value. Influencing causal factor from plurality of predefined causal factors are obtained by performing ensemble technique using optimized base hypothesis.