Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics

TL;DR

This study investigates how AI guidance influences user trust, bias, and data exploration in visual analytics, revealing that task difficulty affects suggestion acceptance and that transparency levels have limited impact.

Contribution

It provides empirical insights into user behavior and trust in AI-guided visual analytics, highlighting the effects of suggestion accuracy, transparency, and task difficulty.

Findings

Participants accepted more suggestions during harder tasks despite lower AI accuracy.

Transparency levels did not significantly influence suggestion usage or trust.

Suggestion use led to greater data exploration in terms of quantity and diversity.

Abstract

The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the AI's lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness of AI-guided VA tools.