| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
|
| Initial Amendment Date: | August 22, 2024 |
| Latest Amendment Date: | August 22, 2024 |
| Award Number: | 2339427 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Raj Acharya
racharya@nsf.gov (703)292-7978 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 15, 2024 |
| End Date: | July 31, 2029 (Estimated) |
| Total Intended Award Amount: | $500,000.00 |
| Total Awarded Amount to Date: | $250,000.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
341 PINE TREE RD ITHACA NY US 14850-2820 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | Info Integration & Informatics |
| Primary Program Source: |
01002526DB NSF RESEARCH & RELATED ACTIVIT 01002627DB NSF RESEARCH & RELATED ACTIVIT 01002728DB NSF RESEARCH & RELATED ACTIVIT 01002829DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Essential societal decisions and allocations rely on imperfect crowdsourced data. For example, in resident crowdsourcing, the public reports problems (for example, fallen trees and power lines and flooding after storms) that the government needs to address. State and national public health decisions (for example, allocation of vaccines and resources) rely on individual testing and reporting. However, decisions made without incorporating what is known regarding the deficiencies of crowdsourced data can be wasteful. For example, past work has shown that data from disadvantaged areas may be systematically missing, leading to under-allocation of resources there. This research project will improve public decision-making by developing statistical methods to understand differential reporting behavior and engineer more efficient, transparent systems that account for missing information and heterogeneous behavior. This knowledge will help government and public-interest organizations allocate resources where they are most needed. This project will also educate data scientists and researchers for the public interest, including by providing continuing education and publicly available resources for municipal technology workers and open data hobbyists.
This research pursues three objectives: 1) measuring biases in report data, with a focus on public crowdsourcing of incidents and community health monitoring, where needs are spatially correlated and varied; 2) auditing responses to reports when resources are capacity constrained and multi-stage; 3) in collaboration with government and non-profit decision-makers, improving every stage of the response pipeline in practice. The research will contribute methods for general Bayesian inference, optimization, machine learning, and data-driven decision-making , applied to auditing and engineering systems in complex environments, including education, health, and government broadly.
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.
