Generative artificial intelligence (AI) tools can now help people perform complex data science tasks regardless of their expertise. While these tools have great potential to help more people work with data, their end-to-end approach does not support users in evaluating alternative approaches and reformulating problems, both critical to solving open-ended tasks in high-stakes domains. In this paper, we reflect on two AI data science systems designed for the medical setting and how they function as tools for thought. We find that success in these systems was driven by constructing AI workflows around intentionally-designed intermediate artifacts, such as readable query languages, concept definitions, or input-output examples. Despite opaqueness in other parts of the AI process, these intermediates helped users reason about important analytical choices, refine their initial questions, and contribute their unique knowledge. We invite the HCI community to consider when and how intermediate artifacts should be designed to promote effective data science thinking.

翻译：生成式人工智能工具现已能帮助人们完成复杂的数据科学任务，无论其专业水平如何。尽管这些工具在帮助更多人处理数据方面潜力巨大，但其端到端的方法未能支持用户评估替代方案和重新定义问题，而这对于解决高风险领域的开放式任务至关重要。本文反思了两款专为医疗场景设计的AI数据科学系统，并探讨它们如何作为思维工具发挥作用。我们发现，这些系统的成功源于围绕刻意设计的中间产物（如可读查询语言、概念定义或输入输出示例）构建AI工作流。尽管AI流程的其他部分可能不透明，但这些中间产物帮助用户推理重要的分析选择、完善初始问题并贡献其独特知识。我们邀请人机交互社区思考：何时以及如何设计中间产物，以促进有效的数据科学思维。