El-Assady Mennatallah, Moruzzi Caterina
IEEE Comput Graph Appl. 2022 Nov-Dec;42(6):11-23. doi: 10.1109/MCG.2022.3200328. Epub 2022 Dec 13.
Collaborative human-AI problem-solving and decision making rely on effective communications between both agents. Such communication processes comprise explanations and interactions between a sender and a receiver. Investigating these dynamics is crucial to avoid miscommunication problems. Hence, in this article, we propose a communication dynamics model, examining the impact of the sender's explanation intention and strategy on the receiver's perception of explanation effects. We further present potential biases and reasoning pitfalls with the aim of contributing to the design of hybrid intelligence systems. Finally, we propose six desiderata for human-centered explainable AI and discuss future research opportunities.
人机协作解决问题和决策依赖于双方之间的有效沟通。这种沟通过程包括发送者和接收者之间的解释和互动。研究这些动态过程对于避免沟通不畅问题至关重要。因此,在本文中,我们提出了一种沟通动态模型,研究发送者的解释意图和策略对接收者对解释效果的感知的影响。我们还指出了潜在的偏差和推理陷阱,旨在为混合智能系统的设计提供帮助。最后,我们提出了以人类为中心的可解释人工智能的六个要求,并讨论了未来的研究机会。