METHOD AND SYSTEM FOR GENERATING CONTEXTUAL EXPLANATION FOR MODEL PREDICTIONS

Human-understandable explanations of Artificial Intelligence (AI) based models are crucial to building transparency and trust in AI based solutions. More importantly, these explanations need to be contextual, applicable to the domain the model is used in and relevant to the concerned stakeholder. Conventionally, there is a lack of communicating these explanations to various stakeholders in a language that they can understand and relate to. The present disclosure facilitates the conversational agents (chat bots) with intelligence and actions that would help them communicate the right information to the right stakeholder in the right way. In the present disclosure, contextual explanation for user queries is generated based on the output from AI models. Here, the impacting features are obtained from the explainer model associated with the prediction model and the contextual information is generated. Further, the contextual information is converted to the contextual explanation to the user.