FAI: Building a Fair Recommender System for Foster Care Services within the Constraints of a Sociotechnical System

在美国，有超过40万的青少年被寄养，每年有超过2万的青少年在没有与家人团聚的情况下离开寄养。这些年轻人的结果很糟糕。到19岁时，只有59%的人能完成高中学业，20%的人无家可归，27%的男性被关进监狱。这些结果对青年和社会产生了重大和代价高昂的影响。幸运的是，被寄养的年轻人可能有资格接受服务，比如职业培训，这可能会提高他们获得积极生活成果的机会。然而，社会工作者通常只有在青年经历了相关的需求或危机之后才能为这些服务确定青年。为了解决这个问题，该项目将开发一种算法，以帮助社会工作者在危机发生之前识别需要服务的青少年，并以公平公正的方式分配这些服务。关键的是，该算法在每一步都将得到来自寄养青年和社会工作者的信息。所开发的方法将为长期资金不足和人员不足的寄养机构释放资源，提高决策程序的透明度和问责制，并提供一个关键工具，在危机发生之前确定需要服务的青年。在开发服务推荐算法时，需要解决三个基本的研究目标。首先，将开发方法来帮助领域专家识别和减轻训练数据中难以发现的社会偏见。其次，将开发方法，以确定有关服务分配决策的公平性的多个角度（例如，从寄养青年和个案工作者的角度）。最后，将开发一种方法，以平衡确保公平分配的目标，根据对公平的多种观点，以及增加所有青年积极生活成果的可能性的目标，为培养青年推荐服务分配策略。将广泛评估所有方法，并与寄养中的多个利益攸关方紧密结合。我们还将提供证据，证明我们的方法在其他领域的通用性，以及它们的效率和准确性的可证明界限。利益相关者的整合包括与当地一家每年为超过10,000名青年提供服务的寄养机构建立伙伴关系，以及一个由具有寄养经验的青年组成的青年咨询委员会，该委员会将在模式开发中发挥重要作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There are more than 400,000 youth in foster care in the US, and each year more than 20,000 age out of foster care without reunifying with their families. The outcomes for these youth are poor. By age 19, only 59% finish high school, 20% have been homeless, and 27% of males have been incarcerated. These outcomes have significant and costly impacts for the youth and for society. Fortunately, youth in foster care are potentially eligible to receive services, such as vocational training, that may improve their chances at positive life outcomes. However, caseworkers are typically only able to identify youth for these services after the youth experiences a relevant need or crisis. To address this problem, this project will develop an algorithm to assist caseworkers in identifying youth in need of services before crises occur, and to allocate those services in a fair and just way. Critically, the algorithm will be informed at every step by inputs from foster youth and caseworkers. The methods developed will free resources for foster care agencies that are chronically underfunded and understaffed, increase procedural transparency and accountability in decision-making, and provide a critical tool to identify youth who need services before crises occur.In developing the service recommendation algorithm, three fundamental research objectives will be addressed. First, methods will be developed to help domain experts identify and mitigate hard-to-find social biases in training data. Second, methods will be developed to identify multiple perspectives of fairness (e.g. from foster youth and case workers) with respect to service allocation decisions. Finally, a method will be developed to recommend service allocation strategies for foster youth in ways that balance the goal of ensuring fair distribution, according to multiple perspectives on fairness, and the goal of increasing the odds of positive life outcomes of all youth. All approaches will be evaluated extensively, with tight integration with multiple stakeholders in foster care. We will also provide evidence of the generality of our methods to other domains and provable bounds on their efficiency and accuracy. Stakeholder integration includes a partnership with a local foster care agency servicing over 10,000 youth per year, and a Youth Advisory Council of youth with experience in foster care who will play an important role in model development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.