Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping. Based on a user's prompt, Farsight highlights news articles about relevant AI incidents and allows users to explore and edit LLM-generated use cases, stakeholders, and harms. We report design insights from a co-design study with 10 AI prototypers and findings from a user study with 42 AI prototypers. After using Farsight, AI prototypers in our user study are better able to independently identify potential harms associated with a prompt and find our tool more useful and usable than existing resources. Their qualitative feedback also highlights that Farsight encourages them to focus on end-users and think beyond immediate harms. We discuss these findings and reflect on their implications for designing AI prototyping experiences that meaningfully engage with AI harms. Farsight is publicly accessible at: https://PAIR-code.github.io/farsight.

翻译：基于提示的大语言模型（LLM）交互界面使构建AI驱动的应用程序原型变得前所未有地便捷。然而，识别AI应用可能产生的潜在危害仍然是一项挑战，尤其是在基于提示的原型设计中。为解决这一问题，我们提出了Farsight——一种新颖的原位交互式工具，帮助用户识别正在原型设计的AI应用中的潜在危害。基于用户的提示，Farsight会高亮显示相关AI事故的新闻文章，并允许用户探索和编辑LLM生成的用例、利益相关者及危害。我们报告了与10名AI原型设计者共同开展的设计研究所得洞察，以及针对42名AI原型设计者的用户研究结果。在使用Farsight后，参与我们用户研究的AI原型设计者能够更独立地识别与提示相关的潜在危害，并认为我们的工具比现有资源更实用、更易用。其定性反馈还强调，Farsight促使他们关注最终用户，并超越即时危害进行思考。我们讨论了这些发现，并反思了其对设计能够切实应对AI危害的原型设计体验的意义。Farsight可通过以下链接公开访问：https://PAIR-code.github.io/farsight。