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:随着人工智能和数据挖掘技术的发展,群体剖析和群体级别分析在许多领域中得到了越来越广泛的应用,包括政策制定和直接营销。 在某些情况下,从数据中提取的统计数据可能提供有关群体共享特征的洞见; 在其他情况下,群体级别的分析可能导致问题,包括定型和系统性压迫。如何使分组分析的过程更加审慎? 在这项工作中,我们确定了一组负责任的群体分析设计准则,以阐明群体分化的需要,以及防止对群体的过度概括。遵循设计准则,我们开发了TribalGram,一个可解释机器学习算法和可视化利用来提供推断评估,模型解释,数据协作和感知的视觉分析套件。通过与专业人士的访谈,我们展示了我们的设计和工具如何带来对从数据中挖掘出来的“群体”的更深入理解。