Tribe or Not? Critical Inspection of Group Differences Using TribalGram

• URL: http://arxiv.org/abs/2303.09664v1
• Date: Thu, 16 Mar 2023 21:47:48 GMT
• Title: Tribe or Not? Critical Inspection of Group Differences Using TribalGram
• Authors: Yongsu Ahn, Muheng Yan, Yu-Ru Lin, Wen-Ting Chung, Rebecca Hwa
• Abstract summary: Group profiling and group-level analysis have been increasingly used in many domains including policy making and direct marketing. In this work, we identify a set of accountable group analytics design guidelines to explicate the needs for group differentiation and preventing overgeneralization of a group. Following the design guidelines, we develop TribalGram, a visual analytic suite that leverages interpretable machine learning algorithms and visualization.
• Score: 13.670327096294479
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a group's shared characteristics; in others, the group-level analysis can lead to problems including stereotyping and systematic oppression. How can analytic tools facilitate a more conscientious process in group analysis? In this work, we identify a set of accountable group analytics design guidelines to explicate the needs for group differentiation and preventing overgeneralization of a group. Following the design guidelines, we develop TribalGram, a visual analytic suite that leverages interpretable machine learning algorithms and visualization to offer inference assessment, model explanation, data corroboration, and sense-making. Through the interviews with domain experts, we showcase how our design and tools can bring a richer understanding of "groups" mined from the data.

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