论文标题
共同信息评分：通过应用于儿童福利数据的应用程序，提高分类聚类任务的可解释性
Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data
论文作者
论文摘要
从无家可归到监禁，美国寄养系统中的青年比同龄人面临许多负面生活结果的可能性要大得多。有关这些年轻人的行政数据有可能提供见解，以帮助确定改善其迈向更好生活的方式的方法。但是，从缺失的数据到系统性不平等的反射，这些数据也遭受了多种偏见。目前的工作提出了一种新颖的规范性方法，用于使用这些数据来提供有关数据偏见以及他们跟踪的系统和青年的见解。具体而言，我们开发了一种新颖的分类聚类和群集摘要方法，该方法使我们能够深入了解寄养青年现有数据的微妙偏见，并提供有关进一步（通常是定性）研究的洞察力，以识别帮助青年的潜在方法。
Youth in the American foster care system are significantly more likely than their peers to face a number of negative life outcomes, from homelessness to incarceration. Administrative data on these youth have the potential to provide insights that can help identify ways to improve their path towards a better life. However, such data also suffer from a variety of biases, from missing data to reflections of systemic inequality. The present work proposes a novel, prescriptive approach to using these data to provide insights about both data biases and the systems and youth they track. Specifically, we develop a novel categorical clustering and cluster summarization methodology that allows us to gain insights into subtle biases in existing data on foster youth, and to provide insight into where further (often qualitative) research is needed to identify potential ways of assisting youth.
