可解释AI用于盲人和低视力用户：在智能体时代导航信任、模态和可解释性
Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
AI总结：
本文探讨了盲人和低视力用户在智能体时代对可解释AI的独特需求，指出需多模态界面和责任意识设计以提升可解释性与信任度。
摘要：
可解释人工智能（XAI）对于确保信任和问责至关重要，但其发展仍主要依赖视觉。对于盲人和低视力（BLV）用户，缺乏可访问的解释会阻碍他们独立使用AI驱动的辅助技术。随着AI系统从单次查询工具转变为能够执行多步骤行动和在长期任务范围内做出重大决策的自主代理，单个未检测的错误可能在反馈可用前不可逆地传播。本文通过全面分析用户访谈和当前研究，探讨BLV社区的XAI需求。通过检查环境感知和决策支持中的使用模式，我们发现存在显著的模态差距。实证证据表明，尽管BLV用户高度重视对话解释，但他们经常因AI故障而自责。本文最后提出了一项关于智能体系统中可访问XAI的研究议程，倡导多模态界面、责任意识的解释设计和参与式开发。
英文摘要：
Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools into autonomous agents that take multi-step actions and make consequential decisions across extended task horizons, where a single undetected error can propagate irreversibly before any feedback is available. This paper investigates the unique XAI requirements of the BLV community through a comprehensive analysis of user interviews and contemporary research. By examining usage patterns across environmental perception and decision support, we identify a significant modality gap. Empirical evidence suggests that while BLV users highly value conversational explanations, they frequently experience "self-blame" for AI failures. The paper concludes with a research agenda for accessible Explainable AI in agentic systems, advocating for multimodal interfaces, blame-aware explanation design, and participatory development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
