从二元组到三元组：中风康复中可解释人工智能需求的引出
From Dyad to Triad: Eliciting XAI Requirements in Stroke Rehabilitation
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中文总结 AI 辅助
研究针对中风康复中引出可解释人工智能需求的挑战，提出基于视频的支架协议，包含类比桥接等四种方法，揭示了参与者的需求及引导偏差，为康复中引出面向患者的XAI需求提供可复用方法，是可靠人机系统设计前提。
中文摘要 AI 辅助
从中风幸存者那里引出可解释人工智能（XAI）需求对设计可靠的康复脑机接口具有直接影响，是一项方法学挑战。当患者和护理人员缺乏可解释性概念框架且标准引出方法对后天性沟通障碍患者在结构上不适用时，他们如何表达对算法透明度的偏好？我们提出了一种基于视频的XAI需求引出的支架协议，并在康复环境中进行了开发和试点。在一项有三名中风幸存者（两名中重度失语）和三名护理人员的形成性研究中，引导者使用了四种与视频一起的支架方法：1）类比桥接将人工智能状态映射到熟悉系统；2）投射角色使敏感话题非个性化；3）二元强制减少认知负荷；4）延长响应时间。这些方法成功揭示了参与者之间异质的、有时相互冲突的XAI需求。反思性分析还揭示了三种系统性引导偏差，即规范偏差、假设确认偏差和在场效应，其中支架会无意中影响反应。我们将这些作为从业者的协议风险指南。该协议和指南共同构成了在康复中引出面向患者的XAI需求的可重复使用的方法学贡献，认为这种引出是设计可靠人机系统的必要前提，而非可选的初步工作。
英文摘要
Eliciting explainable AI (XAI) requirements from stroke survivors presents a methodological challenge with direct implications for the design of trustworthy brain-computer interfaces for rehabilitation. How can patients and caregivers articulate preferences about algorithmic transparency when they lack conceptual frameworks for explainability, and when standard elicitation approaches are structurally inadequate for users with acquired communication disorders? We present a video-based scaffolding protocol for XAI requirements elicitation, developed and piloted in a rehabilitation context. In a formative study with three stroke survivors (two with moderate-to-severe aphasia) and three caregivers, facilitators employed four scaffolding approaches alongside the videos: 1) analogical bridging mapping AI states to familiar systems, 2) projective personas depersonalising sensitive topics, 3) binary forcing reducing cognitive load, and 4) extended response time. These approaches successfully surfaced heterogeneous, sometimes conflicting XAI needs across participants. Reflexive analysis additionally revealed three systematic facilitation biases, namely, normative bias, hypothesis confirmation bias, and presence effect, where scaffolding inadvertently shaped responses. We present these as protocol risk guidelines for practitioners. Together, the protocol and guidelines constitute a reusable methodological contribution for eliciting patient-facing XAI requirements in rehabilitation, arguing that such elicitation is a necessary prerequisite for trustworthy human-machine systems design, not an optional preliminary.
发表机构
- IIT Gandhinagar（印度理工学院甘地纳格尔分校）
机构由 AI 辅助整理，请以论文原文为准。
