Behave-XAI: Deep Explainable Learning of Behavioral Representational Data
This addresses the need for explainable AI in digital platforms to enhance user trust and satisfaction, though it appears incremental by applying existing XAI methods to a specific behavioral data scenario.
The paper tackles the problem of making AI decisions understandable in behavioral mining by developing a deep learning model that uses explainable AI (XAI) to provide explanations for user engagement predictions based on sensor data, resulting in user trials that assess preference and credibility of these explanations.
According to the latest trend of artificial intelligence, AI-systems needs to clarify regarding general,specific decisions,services provided by it. Only consumer is satisfied, with explanation , for example, why any classification result is the outcome of any given time. This actually motivates us using explainable or human understandable AI for a behavioral mining scenario, where users engagement on digital platform is determined from context, such as emotion, activity, weather, etc. However, the output of AI-system is not always systematically correct, and often systematically correct, but apparently not-perfect and thereby creating confusions, such as, why the decision is given? What is the reason underneath? In this context, we first formulate the behavioral mining problem in deep convolutional neural network architecture. Eventually, we apply a recursive neural network due to the presence of time-series data from users physiological and environmental sensor-readings. Once the model is developed, explanations are presented with the advent of XAI models in front of users. This critical step involves extensive trial with users preference on explanations over conventional AI, judgement of credibility of explanation.