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。

透过终端用户启发式设计超越 XAI 算法边界

Transcending XAI Algorithm Boundaries through End-User-Inspired Design

With Human-Centric Research (HCR) we can steer research activities so that
the research outcome is beneficial for human stakeholders, such as end users.
But what exactly makes research human-centric? We address this question by
providing a working definition and define how a research pipeline can be split
into different stages in which human-centric components can be added.
Additionally, we discuss existing NLP with HCR components and define a series
of guiding questions, which can serve as starting points for researchers
interested in exploring human-centric research approaches. We hope that this
work would inspire researchers to refine the proposed definition and to pose
other questions that might be meaningful for achieving HCR.

提出了人本研究的定义，并介绍了人本研究措施可以添加到研究管道的不同阶段，讨论了具有人本组件的现有 NLP，并定义了一系列引导性问题，以探索人本研究方法。希望此工作能够启发研究者完善所提出的定义，并提出其他有意义的问题，以实现 HCR。