
NSF Org: |
SES Division of Social and Economic Sciences |
Recipient: |
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Initial Amendment Date: | April 9, 2019 |
Latest Amendment Date: | July 6, 2023 |
Award Number: | 1848286 |
Award Instrument: | Continuing Grant |
Program Manager: |
Wenda K. Bauchspies
wbauchsp@nsf.gov (703)292-5034 SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
Start Date: | July 1, 2019 |
End Date: | January 31, 2026 (Estimated) |
Total Intended Award Amount: | $400,300.00 |
Total Awarded Amount to Date: | $400,300.00 |
Funds Obligated to Date: |
FY 2020 = $103,652.00 FY 2021 = $47,690.00 FY 2022 = $53,350.00 FY 2023 = $87,913.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
Sponsor Congressional District: |
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Primary Place of Performance: |
313 Morrill Hall Ithaca NY US 14853-4701 |
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): | STS-Sci, Tech & Society |
Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT 01002223DB NSF RESEARCH & RELATED ACTIVIT 01002324DB NSF RESEARCH & RELATED ACTIVIT |
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): | 47.075 |
ABSTRACT
Understanding the social consequences of algorithmic systems has become a key concern for policy makers, engineers, and academics due to reports of bias, discrimination, and misrepresentation in areas such as credit scoring, hiring, and policing. This project will focus on understanding how ordinary citizens are affected by, cope with, and challenge algorithmic systems. The investigator will do so by using qualitative, historical, and ethnographic methods to understand how people interact with web search engines, the effects search engine optimization schemas, and how the situation of those who have been negatively affected by algorithmic systems might be improved. In addition to primary research, this proposal will fund an intervention among multidisciplinary teams of graduate students to educate next generation experts to address issues of fair representation and accountability in algorithmic systems.
Algorithmic systems are a pervasive aspect of modern life in areas such as web searches, hiring decisions, credit rankings, and determining the cost of health insurance policies. Important social issues arise when the data and information produced by algorithmic systems turns out to be inaccurate, biased, or discriminatory. This situation is made more complicated because algorithmic systems are "black boxes," the inner workings of which are often kept secret for proprietary reasons. This project will investigate how algorithmic systems shape the lives of ordinary citizens, and how citizens work to cope with and challenge them. Study 1 will combine in-depth interviews and self-reflections to understand the lived experiences of data subjects. Study 2 will blend document analysis and oral history interviews to detail the history of search engine optimization. Study 3 will rely on ethnography participant observation to investigate the ethics of algorithmic design. Data and insights from these studies will then be used to create an educational intervention aimed at informing graduate students from relevant fields about fair representation and accountability in algorithmic systems.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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