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Computer Science > Human-Computer Interaction

arXiv:2309.10947 (cs)
[Submitted on 19 Sep 2023 (v1), last revised 4 Mar 2024 (this version, v2)]

Title:How Do Analysts Understand and Verify AI-Assisted Data Analyses?

Authors:Ken Gu, Ruoxi Shang, Tim Althoff, Chenglong Wang, Steven M. Drucker
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Abstract:Data analysis is challenging as it requires synthesizing domain knowledge, statistical expertise, and programming skills. Assistants powered by large language models (LLMs), such as ChatGPT, can assist analysts by translating natural language instructions into code. However, AI-assistant responses and analysis code can be misaligned with the analyst's intent or be seemingly correct but lead to incorrect conclusions. Therefore, validating AI assistance is crucial and challenging. Here, we explore how analysts understand and verify the correctness of AI-generated analyses. To observe analysts in diverse verification approaches, we develop a design probe equipped with natural language explanations, code, visualizations, and interactive data tables with common data operations. Through a qualitative user study (n=22) using this probe, we uncover common behaviors within verification workflows and how analysts' programming, analysis, and tool backgrounds reflect these behaviors. Additionally, we provide recommendations for analysts and highlight opportunities for designers to improve future AI-assistant experiences.
Comments: Accepted to CHI 2024
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2309.10947 [cs.HC]
  (or arXiv:2309.10947v2 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2309.10947

Submission history

From: Ken Gu [view email]
[v1] Tue, 19 Sep 2023 22:02:34 UTC (26,486 KB)
[v2] Mon, 4 Mar 2024 16:58:22 UTC (32,713 KB)
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