  We present results from a pilot experiment to measure if machine
recommendations can debias human perceptual biases in visualization tasks. We
specifically studied the ``pull-down'' effect, i.e., people underestimate the
average position of lines, for the task of estimating the ensemble average of
data points in line charts. These line charts can show for example temperature
or precipitation in 12 months. Six participants estimated ensemble averages
with or without an AI assistant. The assistant, when available, responded at
three different speeds to assemble the conditions of a human collaborator who
may delay his or her responses. Our pilot study showed that participants were
faster with AI assistance in ensemble tasks, compared to the baseline without
AI assistance. Although ``pull-down'' biases were reduced, the effect of AI
assistance was not statistically significant. Also, delaying AI responses had
no significant impact on human decision accuracy. We discuss the implications
of these preliminary results for subsequent studies.
