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.

翻译：根据人工智能的最新趋势，AI系统需要对其做出的通用、特定决策及其提供的服务进行阐明。只有通过解释（例如，为何在特定时间得出某项分类结果）才能使消费者满意。这促使我们在行为挖掘场景中采用可解释或人类可理解的AI技术，其中用户在数字平台上的参与度由情境因素（如情绪、活动、天气等）决定。然而，AI系统的输出并非总是系统性地正确，即便是系统性正确时也常表现出非完美性，从而引发困惑，例如：为何做出该决策？其背后的原因是什么？在此背景下，我们首先将行为挖掘问题形式化为深度卷积神经网络架构。随后，由于用户生理与环境传感器读数中存在时间序列数据，我们采用递归神经网络。模型开发完成后，通过引入XAI模型向用户呈现解释。这一关键步骤涉及大量试验，以评估用户对解释相较于传统AI的偏好，以及对解释可信度的判断。