Can AI Mitigate Human Perceptual Biases? A Pilot Study
This pilot study explores AI's potential to mitigate human perceptual biases in visualization tasks, but it is incremental with preliminary results.
The study investigated whether AI recommendations could reduce the 'pull-down' perceptual bias in estimating ensemble averages from line charts, finding that participants were faster with AI assistance but the bias reduction was not statistically significant.
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.