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- W4214927243 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." @default.
- W4214927243 created "2022-03-05" @default.
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- W4214927243 date "2022-03-04" @default.
- W4214927243 modified "2023-10-10" @default.
- W4214927243 title "Tribe or Not? Critical Inspection of Group Differences Using TribalGram" @default.
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- W4214927243 doi "https://doi.org/10.1145/3484509" @default.
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