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

FAI：在社会技术系统的约束下建立公平的寄养服务推荐系统

• 批准号: 1939579
• 负责人: Kenneth Joseph
• 金额: $ 45.26万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939579&HistoricalAwards=false
• 关键词: FAI; Building; Fair; Recommender; System

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.

在美国，有超过40万的青少年被寄养，每年有超过2万的青少年没有与家人团聚。这些年轻人的结果很差。到19岁时，只有59%的人完成高中学业，20%的人无家可归，27%的男性被监禁。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，被寄养的青年有可能有资格接受服务，如职业培训，这可能会增加他们获得积极生活成果的机会。 然而，个案工作者通常只能在青少年经历相关需求或危机后才能为这些服务确定青少年。为解决这一问题，该项目将开发一种算法，以协助个案工作者在危机发生前确定需要服务的青年，并以公平和公正的方式分配这些服务。至关重要的是，算法的每一步都将得到寄养青年和个案工作者的输入。开发的方法将释放长期资金不足和人员不足的寄养机构的资源，提高决策过程中的透明度和问责制，并提供一个关键的工具，以确定谁需要服务的青年危机发生之前。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。第二，将制定方法，以确定公平的多个角度（如从寄养青年和个案工作者）与服务分配的决定。最后，将制定一种方法，为寄养青年推荐服务分配战略，根据公平的多种观点，平衡确保公平分配的目标和增加所有青年积极生活成果的可能性的目标。所有方法都将得到广泛的评估，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的一般性，其他领域和可证明的界限，其效率和准确性。利益相关者的整合包括与当地寄养机构的伙伴关系，每年为10，000多名青年提供服务，以及由具有寄养经验的青年组成的青年咨询理事会，他们将在模型开发中发挥重要作用。该奖项反映了NSF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。

项目成果

Strategic Behavior in Two-sided Matching Markets with Recommendation-enhanced Preference-formation

推荐增强偏好形成的双边匹配市场中的战略行为

• 发表时间: 2023
• 期刊: 37th Conference on Neural Information Processing Systems (NeurIPS 2023
• 作者: Ionescu, Stefania;Du, Yuhao;Joseph, Kenneth;Hannak, Aniko
• 通讯作者: Hannak, Aniko

Formative Modeling of Foster Care Work: A Cognitive Work Analysis Approach

寄养工作的形成模型：认知工作分析方法

• DOI: 10.1177/1071181321651023
• 发表时间: 2021
• 期刊: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
• 作者: Wurst, Connor;Chen, Huei-Yen Winnie;Joseph, Kenneth
• 通讯作者: Joseph, Kenneth

Computational Models for Social Good: Beyond Bias and Representation

社会公益的计算模型：超越偏见和代表性

• 发表时间: 2022
• 期刊: SBP-BRiMs'22
• 作者: Dancy, Christopher L;Joseph, Kenneth
• 通讯作者: Joseph, Kenneth

A Data-Driven Simulation of the New York State Foster Care System

纽约州寄养系统的数据驱动模拟

• DOI: 10.1145/3531146.3533165
• 发表时间: 2022
• 期刊: and Transparency
• 作者: Du, Yuhao;Ionescu, Stefania;Sage, Melanie;Joseph, Kenneth
• 通讯作者: Joseph, Kenneth

A qualitative, network-centric method for modeling socio-technical systems, with applications to evaluating interventions on social media platforms to increase social equality

一种以网络为中心的定性方法，用于对社会技术系统进行建模，可用于评估社交媒体平台上的干预措施，以提高社会平等

• DOI: 10.1007/s41109-022-00486-8
• 发表时间: 2022
• 期刊: Applied Network Science
• 影响因子: 2.2
• 作者: Joseph, Kenneth;Chen, Huei-Yen Winnie;Ionescu, Stefania;Du, Yuhao;Sankhe, Pranav;Hannak, Aniko;Rudra, Atri
• 通讯作者: Rudra, Atri