XAI

To this day, various approaches for providing local explanation of black box machine learning models have been introduced. Despite these efforts, existing methods suffer from deficiencies such as being difficult to comprehend, only considering one feature at a time and disregarding inter-feature dependencies, lacking meaningful values for each feature, or only highlighting features that support the model’s decision. To overcome these drawbacks, this study presents a new approach to explain the predictions of any black box classifier, called Monte Carlo tree search for eXplainable Artificial Intelligence (McXai).

In recent years, there has been a growing trend in the utilization of Artificial Intelligence (AI) technology to construct human-centered systems that are based on implicit time series information, ranging from contextual recommendations on smartwatches to human activity recognition on production workshop. Despite the advantages of these systems, the opaqueness and unpredictability of these systems for users has elicited concerns. In this paper, we propose a novel explanation method named State-graph Based eXplanable Artificial Intelligent (SBXAI), which exhibits the sequential relationship between time periods through directed circular graphs while emphasizing the importance of each time period in an instance.