Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems

• URL: http://arxiv.org/abs/2106.03775v1
• Date: Mon, 7 Jun 2021 16:38:43 GMT
• Title: Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems
• Authors: Jeff Druce, Michael Harradon, James Tittle
• Abstract summary: We offer an explainable artificial intelligence (XAI) framework that provides a three-fold explanation. We created a user-interface for our XAI framework and evaluated its efficacy via a human-user experiment.
• Score: 0.8701566919381223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of providing users of deep Reinforcement Learning (RL) based systems with a better understanding of when their output can be trusted. We offer an explainable artificial intelligence (XAI) framework that provides a three-fold explanation: a graphical depiction of the systems generalization and performance in the current game state, how well the agent would play in semantically similar environments, and a narrative explanation of what the graphical information implies. We created a user-interface for our XAI framework and evaluated its efficacy via a human-user experiment. The results demonstrate a statistically significant increase in user trust and acceptance of the AI system with explanation, versus the AI system without explanation.

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