Increasing Awareness of Human Biases during Visual Data Analysis using Visual and Haptic Feedback

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Narechania, Arpit
Paden, Jamal
Endert, Alex
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Human biases impact the way people analyze data and make decisions. Women denied C-suite promotions (gender bias), ailing but younger people denied optimal treatment (age bias), dark-skinned people denied parole (racial bias), etc. are examples of biases rampant in the world. Visual data analysis tools such as Tableau and Excel help users see and understand their data but do not report potential biases exhibited by users (e.g., an overemphasis on the Age attribute). Existing research tools have explored visual means (e.g., highlighting the Age attribute to appear darker than other attributes) to increase users' awareness about (biased) analytic behaviors. We believe that using a single, "visual" modality to present such information is a passive type of guidance that only burdens the user's perception skills, which are already engaged to perform the analysis task. We investigate how a more active type of guidance, a combination of "visual" and "haptic" modalities, can better guide the user. We present a visual data analysis system, wired to a haptic gaming mouse. This enhanced system tracks a user's interactions with data and presents them back via haptic feedback (e.g., "buzz"es or vibrates the mouse whenever bias is detected). Through an exploratory user study, we find that these dual guidance modalities can sometimes actively stimulate and engage the user's attention, making them more aware of their analytic behaviors. However, we also find that the haptic feedback can also distract the user, informing the design of future multimodal guidance-enriched user interfaces.
NSF grant IIS-1813281
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