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.

翻译：随着人工智能与数据挖掘技术的兴起，群体画像与群体级分析已被广泛应用于包括政策制定与直接营销在内的诸多领域。数据中提取的统计信息有时可揭示群体的共同特征，但群体级分析也可能引致刻板印象与系统性压迫等问题。分析工具如何促进群体分析中更具责任感的实践？本研究识别出一套可问责的群体分析设计准则，以阐明群体差异化需求并防止群体泛化。基于该设计准则，我们开发了TribalGram——一套融合可解释机器学习算法与可视化的视觉分析套件，支持推理评估、模型解释、数据佐证与意义建构。通过与领域专家的访谈，我们展示了本设计与工具如何促进对数据中挖掘所得"群体"的更深刻理解。