Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition

TL;DR

This paper introduces two interactive task decomposition methods, Stepwise and Phasewise, to improve user control and verification in AI-assisted data analysis, addressing challenges identified in a formative study.

Contribution

It presents novel interactive approaches for guiding AI in data analysis, enhancing control, intervention, and verification compared to baseline methods.

Findings

Users reported greater control with the new systems

Intervention and correction were easier with Stepwise and Phasewise

The approaches improved verification in AI-assisted data analysis

Abstract

LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the challenging task of data analysis programming, which requires expertise in data processing, programming, and statistics. However, our formative study (n=15) uncovered serious challenges in verifying AI-generated results and steering the AI (i.e., guiding the AI system to produce the desired output). We developed two contrasting approaches to address these challenges. The first (Stepwise) decomposes the problem into step-by-step subgoals with pairs of editable assumptions and code until task completion, while the second (Phasewise) decomposes the entire problem into three editable, logical phases: structured input/output assumptions, execution plan, and code. A controlled, within-subjects experiment (n=18) compared these systems against a conversational baseline. Users reported significantly greater control with the Stepwise and Phasewise systems, and found intervention, correction, and verification easier, compared to the baseline. The results suggest design guidelines and trade-offs for AI-assisted data analysis tools.