对话式导航中生成模型易诱导误导，需用可验证架构保障可信性。
The Scenic Route to Deception: Dark Patterns and Explainability Pitfalls in Conversational Navigation
- 用神经符号架构将生成模型与可验证路径算法结合
- 发现对话导航存在故意欺骗与解释不清两类风险
- 适合关注AI导航可信性与安全设计的研究者
随着行人导航越来越多地采用生成式AI，尤其是大语言模型，路线规划的风险正从可验证的几何任务演变为模糊且具有说服力的对话过程。尽管对话界面提供个性化体验，但也引入了操纵与错误信任的风险。我们基于意图与来源构建二维框架，区分了故意欺骗（暗黑模式）与无意伤害（可解释性陷阱）。提出缝合式设计策略以缓解这些危害。建议通过神经符号架构实现可信对话导航：让可验证的路径算法为生成式AI的说服能力提供基础，确保系统在解释路线时，同样清晰说明自身的局限与动机。
原文摘要 · Abstract (English)
As pedestrian navigation increasingly experiments with Generative AI, and in particular Large Language Models, the nature of routing risks transforming from a verifiable geometric task into an opaque, persuasive dialogue. While conversational interfaces promise personalisation, they introduce risks of manipulation and misplaced trust. We categorise these risks using a 2x2 framework based on intent and origin, distinguishing between intentional manipulations (dark patterns) and unintended harms (explainability pitfalls). We propose seamful design strategies to mitigate these harms. We suggest that one robust way to operationalise trustworthy conversational navigation is through neuro-symbolic architecture, where verifiable pathfinding algorithms ground GenAI's persuasive capabilities, ensuring systems explain their limitations and incentives as clearly as they explain the route.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。