반 사실적 설명을 사용한 인공지능 시스템 설명 인터페이스 개발

초록

To enhance the trust and usability of the AI system, recent studies have adopted the concept of explainable artificial intelligence(XAI) and proposed explanation methods for the system’s result. One way to understand AI output is to utilize a counterfactual explanation. However, existing methods cannot properly address categorical features. In addition, human cannot analyze approximated decision boundary represented by counterfactual cases with complex features. In this paper, we extend counterfactual explanation with three sequential steps why not, how to, what if reflecting human causal cognition. To find the potential of the suggested interface, we perform a simple scenario-based experiment, where users communicate with an AI system’s results for loan prediction. The experiment results indicate that our interface had better performance in trustworthiness, usefulness, and satisfaction to　the AI system, compared to an existing interface.