Tribe or Not? Critical Inspection of Group Differences Using TribalGram
This work addresses the need for more conscientious group analysis in domains like policy making and marketing to prevent overgeneralization and stereotyping, though it is incremental in applying existing interpretable methods to this specific problem.
The paper tackles the problem of group profiling in data mining leading to stereotyping and oppression by proposing accountable design guidelines and developing TribalGram, a visual analytic suite that uses interpretable machine learning and visualization to enhance group analysis, as validated through expert interviews.
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.