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.

我们通过一个试验研究来衡量机器推荐是否可以减少人类在可视化任务中的感知偏差。我们特别研究了 “下拉” 效应，即人们对直线的平均位置进行低估，在线图中估计数据点的整体平均值任务。这些线图可以用来显示例如 12 个月的温度或降水情况。通过六名参与者的实验估计整体平均值，并比较了有无人工智能助手的情况。实验结果显示，在整体任务中，有人工智能助手的参与者比没有助手的参与者更快。虽然 “下拉” 偏差减小，但人工智能助手的效果在统计上没有显著差异。同时，人工智能响应的延迟对人类的决策准确性也没有显著影响。我们讨论了初步结果对后续研究的影响。