Mitigating Exploration Biases during Visual Data Analysis
Georgia Institute of Technology, Emory UniversityHi, Welcome to the Lumos Organization!
Visual data analysis tools provide people with the agency and flexibility to explore data using a variety of interactive functionality. However, this flexibility may introduce potential consequences in situations where users unknowingly over- or under-emphasize specific subsets of the data or attribute space they are analyzing. For example, users may overemphasize specific attributes and/or their values (e.g., Gender is always encoded on the X axis), underemphasize others (e.g., Religion is never encoded), ignore a subset of the data (e.g., older people are filtered out), etc.
To increase awareness of and mitigate such "exploration biases", we have thus far undertaken three projects: BiasBuzz (ACM CHI LBW'24); Left, Right, and Gender (IEEE VIS'21); and Lumos (IEEE VIS'21). Check them out!!!
BiasBuzz
Combining Visual Guidance with Haptic Feedback
Jamal Paden*, Arpit Narechania*, Alex Endert
During visual data analysis, users may inadvertently focus more on certain aspects of data, affecting analysis outcome(s). Existing tools primarily rely on visual cues (e.g., highlight already visited data) to increase user awareness of such analytic behaviors. We believe this single, visual modality is a passive form of guidance that adds to users' cognitive load already engaged in analysis. We investigate how a dual modality (visual guidance and haptic feedback) can capture users' attention and more actively guide them in their pursuits. We interface an existing visual data analysis tool with a gaming mouse. This enhanced system tracks user interactions and communicates biases by vibrating the mouse (haptic) and simultaneously displaying contextual information in the tool (visual). A formative study with nine users revealed that this dual modality increased analytical awareness in some cases but some users found the haptic mouse vibrations to be distracting and disturbing, informing the design of future multimodal user interfaces.
Citation:
@article{paden2024biasbuzz,
title={{BiasBuzz: Combining Visual Guidance with Haptic Feedback to Increase Awareness of Analytic Behavior during Visual Data Analysis}},
author={Paden, Jamal and Narechania, Arpit and Endert, Alex},
journal={ACM CHI LBW},
year={2024},
publisher={ACM},
url={https://doi.org/10.1145/3613905.3651064}
}
Left, Right, and Gender
Exploring Interaction Traces to Mitigate Human Biases
Emily Wall*, Arpit Narechania*, Adam Coscia, Jamal Paden, Alex Endert
Human biases impact the way people analyze data and make decisions. Recent work has shown that some visualization designs can better support cognitive processes and mitigate cognitive biases (i.e., errors that occur due to the use of mental "shortcuts"). In this work, we explore how visualizing a user's interaction history (i.e., which data points and attributes a user has interacted with) can be used to mitigate potential biases that drive decision making by promoting conscious reflection of one's analysis process. Given an interactive scatterplot-based visualization tool, we showed interaction history in real-time while exploring data (by coloring points in the scatterplot that the user has interacted with), and in a summative format after a decision has been made (by comparing the distribution of user interactions to the underlying distribution of the data). We conducted a series of in-lab experiments and a crowd-sourced experiment to evaluate the effectiveness of interaction history interventions toward mitigating bias. We contextualized this work in a political scenario in which participants were instructed to choose a committee of 10 fictitious politicians to review a recent bill passed in the U.S. state of Georgia banning abortion after 6 weeks, where things like gender bias or political party bias may drive one's analysis process. We demonstrate the generalizability of this approach by evaluating a second decision making scenario related to movies. Our results are inconclusive for the effectiveness of interaction history (henceforth referred to as interaction traces) toward mitigating biased decision making. However, we find some mixed support that interaction traces, particularly in a summative format, can increase awareness of potential unconscious biases.
Citation:
@article{wall2021lrg,
title={{Left, Right, and Gender: Exploring Interaction Traces to Mitigate Human Biases}},
author={Wall, Emily and Narechania, Arpit and Coscia, Adam and Paden, Jamal and Endert, Alex},
journal={IEEE TVCG},
year={{2022}},
url={https://doi.org/10.1109/TVCG.2021.3114862},
publisher={IEEE}
}
Lumos
Increasing Awareness of Analytic Behavior during Visual Data Analysis
Arpit Narechania, Adam Coscia, Emily Wall, Alex Endert
Lumos is a visual data analysis tool that captures and shows the interaction history with data to increase awareness of such analytic behaviors. Using in-situ (at the place of interaction) and ex-situ (in an external view) visualization techniques, Lumos provides real-time feedback to users for them to reflect on their activities. For example, Lumos highlights datapoints that have been previously examined in the same visualization (in-situ) and also overlays them on the underlying data distribution (i.e., baseline distribution) in a separate visualization (ex-situ). Through a user study with 24 participants, we investigate how Lumos helps users' data exploration and decision-making processes. We found that Lumos increases users' awareness of visual data analysis practices in real-time, promoting reflection upon and acknowledgement of their intentions and potentially influencing subsequent interactions.
Citation:
@article{narechania2021lumos,
title={{Lumos: Increasing Awareness of Analytic Behavior during Visual Data Analysis}},
author={Narechania, Arpit and Coscia, Adam and Wall, Emily and Endert, Alex},
journal={IEEE TVCG},
year={{2022}},
url={https://doi.org/10.1109/TVCG.2021.3114827}],
publisher={IEEE}
}