Explainable Artificial Intelligence 101: Techniques, Applications and Challenges [preprint]

Artificial Intelligence (AI) systems have grown commonplace in modern life, with various applications from customized suggestions to self-driving vehicles. As these systems get more complicated, the necessity for transparency in their decision-making processes becomes more critical. Explainability refers to an AI system’s ability to explain how and why it made a certain judgement or prediction. Recently, there has been a surge of interest in constructing explainable AI (XAI) systems that give insights into the decision-making processes of machine learning models. This paper discloses and elaborates upon a selection of XAI techniques, identifies current challenges and possible future directions in XAI research.

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