  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.
