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SBIR Phase I:Artificial Intelligence and Network Theory for Elections

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2309896
Agency Tracking Number: 2309896
Amount: $275,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AI
Solicitation Number: NSF 23-515
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-10-01
Award End Date (Contract End Date): 2024-09-30
Small Business Information
229 East 28th Street Apt 3D
New York, NY 10016
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Bryant Avila
 (212) 650-6847
 bavila@ccny.cuny.edu
Business Contact
 Bryant Avila
Phone: (212) 650-6847
Email: bavila@ccny.cuny.edu
Research Institution
N/A
Abstract

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project promotes and enhances transparency in the democratic process. It accomplishes this by developing a social awareness system that can detect, understand, and predict opinion trends within a democratic society. Through the development of cutting-edge artificial intelligence (AI) techniques, the project contributes to scientific and technological knowledge by improving the prediction of election results and societal opinion trends with high accuracy. By employing machine learning, the project aims to surpass the limitations of traditional polling methods and provide a real-time predictor of election outcomes worldwide. The project will address the credibility of news on social media serving to strengthen the resilience of the population against misinformation. In addition, the project demonstrates a commitment to inclusivity by actively seeking the participation of underrepresented minorities. _x000D_
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This Small Business Innovation Research (SBIR) Phase I project aims to predict global elections in real-time through the integration of artificial intelligence, network theory, and big data science. By harnessing the power of advanced machine learning models and analyzing vast amounts of publicly expressed opinions on social media, the team offers accurate forecasts of election outcomes. This approach has the potential to disrupt the conventional polling industry, which faces growing uncertainties and challenges such as declining response rates and inherent biases in sampling. The research objectives entail tackling critical research and development challenges, including predicting voter turnout, effectively sampling rural areas with limited online coverage, filtering out bots and fake news sources, inferring the preferences of undecided voters, adjusting sample weights on a state-by-state basis, addressing the opinions of individuals not active on social media, and mitigating social desirability bias (where respondents conceal their intention to vote for controversial candidates). The anticipated technical results involve the development of a transformative machine learning architecture built upon Graph Neural Networks.The framework enables optimized resource allocation and significantly improves the precision of predictions. Ultimately, the results will empower decision-makers with reliable real-time information, facilitating informed choices, and enhancing the resilience of the democratic process._x000D_
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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.

* Information listed above is at the time of submission. *

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