[2603.24877] More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes

Computer Science > Human-Computer Interaction arXiv:2603.24877 (cs) [Submitted on 25 Mar 2026] Title:More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes Authors:Venkatesh Sivaraman, Patrick Vossler, Adam Perer, Julian Hong, Jean Feng View a PDF of the paper titled More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes, by Venkatesh Sivaraman and 4 other authors View PDF HTML (experimental) Abstract: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 interme...