为视障用户设计可解释的AI代理，让盲人也能信任并理解AI决策。
Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
- 通过访谈与研究，发现视障用户需要对话式解释来理解AI行为。
- 用户常因AI错误自责，暴露现有解释系统缺乏责任归属设计。
- 提出参与式开发、多模态界面和责任意识解释框架，适合无障碍设计者参考。
可解释人工智能（XAI）对建立信任和问责至关重要，但其发展仍以视觉为主。对于视障及低视力（BLV）用户而言，缺乏可访问的解释机制成为独立使用AI辅助技术的根本障碍。随着AI系统从单次查询工具转向自主代理，执行多步操作并在长任务周期中做出关键决策，一次未被察觉的错误可能在反馈前持续传播并造成不可逆后果，这一问题愈发严峻。本文通过用户访谈与现有研究的综合分析，探讨了BLV群体在环境感知与决策支持中的独特需求，揭示出显著的模态鸿沟。实证研究表明，尽管BLV用户高度重视对话式解释，却常因系统失败而产生‘自我归责’心理。论文最终提出面向代理系统的可访问XAI研究议程，倡导多模态交互界面、责任意识的解释设计以及参与式开发方法。
原文摘要 · Abstract (English)
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.
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