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

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 across a range of backgrounds and expertise understand and verify the correctness of AI-generated analyses. We develop a design probe that allows analysts to pursue diverse verification workflows using natural language explanations, code, visualizations, inspecting data tables, and performing common data operations. Through a qualitative user study (n=22) using this probe, we uncover common patterns of verification workflows influenced by analysts' programming, analysis, and AI backgrounds. Additionally, we highlight open challenges and opportunities for improving future AI analysis assistant experiences