
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | December 23, 2019 |
Latest Amendment Date: | July 18, 2022 |
Award Number: | 1939579 |
Award Instrument: | Standard Grant |
Program Manager: |
Todd Leen
tleen@nsf.gov (703)292-7215 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | January 1, 2020 |
End Date: | December 31, 2023 (Estimated) |
Total Intended Award Amount: | $452,631.00 |
Total Awarded Amount to Date: | $468,631.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
520 LEE ENTRANCE STE 211 AMHERST NY US 14228-2577 (716)645-2634 |
Sponsor Congressional District: |
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Primary Place of Performance: |
520 Lee Entrance Amherst NY US 14228-2567 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
Fairness in Artificial Intelli, IIS Special Projects |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Youth who age out of the American Foster Care System- that is, those who reach the age at which the system will no longer provide for them- are more likely than youth not in foster care to experience poor life outcomes. For example, nearly half of these youth will be homeless at some point in their life. This project aimed to develop computational tools, in tight collaboration with scholars from Human Factors Engineering and both scholars and practitioners in the field of Social Work, that were useful in helping us to understand why life outcomes for these youth are so poor and what can be done to improve these outcomes and make them more equitable.
Our work has also made significant contributions to intellectual merit in Social Work, Computer Science, and Human Factors Engineering. With respect to Social Work, we have introduced several new (to Social Work) methods to study decision-making and to expose biases in decisions that may lead to inequalities in youth outcomes, and to help explore possible interventions at both the level of the individual organization (e.g. through new user interfaces that help with collaborative decision-making) and at the level of the entire child welfare system (e.g. through the use of exploratory computer simulation models). With respect to Computer Science, the unique constraints of providing services to older youth in care has led us to develop new problems and methods that help in our unique setting, but also turn out to be interesting from a theoretical perspective, and useful in the computational study of other areas of human services where equitable and effective allocation is necessary. Finally, with respect to Human Factors Engineering, we introduce a new sociotechnical domain for human factors to study naturalistic decision-making and human-automation (ML) collaboration in decision-making, and identify new ways to investigate the role of biases in how human decision makers interpret and use automated decision aids.
With respect to broader impacts, our work has made progress in both moving towards a more promising and equitable future for older youth in care. At the national level, we have provided the community with the most comprehensive analysis yet of the factors that impact which youth receive services and what impacts outcomes for these youth, showing that 1) existing services have limited impacts or that existing data is not effective in estimating these effects, and 2) that service allocation patterns are likely driven primarily by state-level policy decisions. At the local level, we have worked with Hillside Agencies to improve the use of data-driven decision-making in the context of therapeutic foster care, and are helping them to build new user interfaces for this purpose.
The project supported the training of four Ph.D. students and twelve UG students (many of whom made fundamental contributions to the project, and nine of which are from Historically Under-represented Groups in Computer Science).
Last Modified: 04/05/2024
Modified by: Kenneth Joseph
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