How to explain AI systems to end users: a systematic literature review and research agenda
通过系统性文献综述，总结了向最终用户解释AI系统的五个目标（可理解性、可信赖性、透明度、可控性和公平性），并提出了设计建议和研究框架，适合AI系统设计者和治理研究者参考。
Purpose Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users. Design/methodology/approach The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review. Findings The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases. Research limitations/implications Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent. Originality/value This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.