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Syst."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>\n            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\u2019s 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\n            <jats:italic>accountable group analytics<\/jats:italic>\n            design guidelines to explicate the needs for group differentiation and preventing overgeneralization of a group. Following the design guidelines, we develop\n            <jats:italic>\n              <jats:monospace>TribalGram<\/jats:monospace>\n            <\/jats:italic>\n            , 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 \u201cgroups\u201d mined from the data.\n          <\/jats:p>","DOI":"10.1145\/3484509","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T10:14:01Z","timestamp":1646388841000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Tribe or Not? 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