AI is powerful, but it can make choices that result in objective errors, contextually inappropriate outputs, and disliked options. We need AI-resilient interfaces that help people be resilient to the AI choices that are not right, or not right for them. To support this goal, interfaces need to help users notice and have the context to appropriately judge those AI choices. Existing human-AI interaction guidelines recommend efficient user dismissal, modification, or otherwise efficient recovery from AI choices that a user does not like. However, in order to recover from AI choices, the user must notice them first. This can be difficult. For example, when generating summaries of long documents, a system's exclusion of a detail that is critically important to the user is hard for the user to notice. That detail can be hiding in a wall of text in the original document, and the existence of a summary may tempt the user not to read the original document as carefully. Once noticed, judging AI choices well can also be challenging. The interface may provide very little information that contextualizes the choices, and the user may fall back on assumptions when deciding whether to dismiss, modify, or otherwise recover from an AI choice. Building on prior work, this paper defines key aspects of AI-resilient interfaces, illustrated with examples. Designing interfaces for increased AI-resilience of users will improve AI safety, usability, and utility. This is especially critical where AI-powered systems are used for context- and preference-dominated open-ended AI-assisted tasks, like ideating, summarizing, searching, sensemaking, and the reading and writing of text or code.

翻译：人工智能功能强大，但其决策可能导致客观错误、情境不当的输出以及用户不喜欢的选项。我们需要具备AI韧性的界面，帮助用户抵御那些不正确或不适合他们的AI决策。为实现这一目标，界面需帮助用户察觉AI决策，并提供适当情境以对其进行合理判断。现有的人机交互指南建议用户高效地驳回、修改或以其他方式摆脱不喜欢的AI决策。然而，用户要摆脱AI决策，首先必须察觉它们的存在——这往往颇具难度。例如，在生成长文档摘要时，系统遗漏了用户极度关注的细节，用户很难发现这一问题。该细节可能隐藏在原始文档的冗长文本中，而摘要的存在会诱使用户不再仔细阅读原文。即便察觉到问题，合理判断AI决策同样可能面临挑战：界面可能极少提供与决策相关的情境信息，用户在决定是否驳回、修改或以其他方式应对AI决策时，可能不得不依赖假设。本文在既有研究基础上，通过实例阐明AI韧性界面的关键特征。设计增强用户AI韧性的界面，将提升AI的安全性、可用性与实用性。这一点对于上下文和偏好主导的开放式AI辅助任务（如构思、摘要、搜索、语义理解、文本或代码的读写）尤为重要。