Lumos: Increasing Awareness of Biases during Visual Data Analysis

Thumbnail Image
Narechania, Arpit
Coscia, Adam
Wall, Emily
Endert, Alex
Associated Organization(s)
Organizational Unit
Supplementary to
Human biases impact the way people analyze data and make decisions. Dark-skinned people denied parole (racial bias), women denied C-suite promotions (gender bias), ailing but younger people denied optimal treatment (age 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). Lumos is an analysis tool that helps users visualize traces of their interactions with data to increase awareness of potential biases. Using in-situ and ex-situ visualization techniques, Lumos provides real-time feedback to users to reflect upon their activities and potentially change future course. For example, Lumos remembers and highlights datapoints that have been previously examined in the same visualizations (in-situ) and overlays the interacted datapoints on the underlying data distribution in a separate visualization (ex-situ). Now sometimes, custom policies rather than biases drive decision-making. For example, a university admissions committee selecting more female than male student applicants can be a conscious choice in abidance to the university's gender-equality policy, rather than an unconscious bias. To address these situations, Lumos allows users to configure custom target distributions and accordingly updates the interaction traces. We believe Lumos can improve data exploration and decision-making scenarios to not only help mitigate the dangers of human biases affecting judgements, but also foster more transparent analysis processes.
NSF IIS-1813281
Date Issued
Resource Type
Resource Subtype
Rights Statement
Rights URI