The boundaries of existing explainable artificial intelligence (XAI)
algorithms are confined to problems grounded in technical users' demand for
explainability. This research paradigm disproportionately ignores the larger
group of non-technical end users, who have a much higher demand for AI
explanations in diverse explanation goals, such as making safer and better
decisions and improving users' predicted outcomes. Lacking
explainability-focused functional support for end users may hinder the safe and
accountable use of AI in high-stakes domains, such as healthcare, criminal
justice, finance, and autonomous driving systems. Built upon prior human factor
analysis on end users' requirements for XAI, we identify and model four novel
XAI technical problems covering the full spectrum from design to the evaluation
of XAI algorithms, including edge-case-based reasoning, customizable
counterfactual explanation, collapsible decision tree, and the verifiability
metric to evaluate XAI utility. Based on these newly-identified research
problems, we also discuss open problems in the technical development of
user-centered XAI to inspire future research. Our work bridges human-centered
XAI with the technical XAI community, and calls for a new research paradigm on
the technical development of user-centered XAI for the responsible use of AI in
critical tasks.

本研究介绍了四个新的可解释人工智能技术问题，这些问题围绕设计和评估算法，并呼吁建立基于人类需求的可解释人工智能技术开发研究范式，以保证人工智能在关键任务中的负责任使用。