用可读中间产物让AI辅助数据科学更透明好用
More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes
- 设计可读查询语言、概念定义等中间产物增强可理解性
- 用户能借助这些产物调整问题、优化分析思路
- 适合医疗等高风险领域需要深度思考的数据任务
生成式人工智能工具如今能让不同背景的人都能完成复杂的数据科学任务。但现有端到端方法难以支持用户评估不同方案或重构问题，而这在高风险领域的开放性任务中至关重要。本文反思了两个面向医疗场景的AI数据科学系统，发现其成功源于围绕有意设计的中间产物构建工作流，如可读查询语言、概念定义或输入输出示例。尽管部分AI过程仍不透明，这些中间产物帮助用户思考关键分析选择、优化初始问题，并融入自身专业知识。本文呼吁人机交互社区关注何时以及如何设计中间产物以促进有效数据科学思维。
原文摘要 · Abstract (English)
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.
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