AI-Resilient Interfaces

The paper discusses the need for AI-resilient interfaces—systems that help users identify and evaluate AI decisions that may be objectively incorrect, contextually inappropriate, or misaligned with user preferences. In human-AI interaction, current guidelines emphasize that users should be able to dismiss, modify, or correct AI-generated outputs. However, a significant challenge lies in the fact that users often fail to notice AI errors initially. For example, when summarizing long documents, AI may omit critical details that users might overlook, relying too heavily on the generated summary instead of thoroughly reviewing the original content. Even when errors are detected, assessing them accurately can be difficult if the interface lacks sufficient contextual information, leading users to rely on assumptions rather than informed decisions.

Read the full paper here: AI-Resilient Interfaces