SBIR Phase I: Artificial Intelligence and Network Theory for Elections

SBIR 第一阶段：选举的人工智能和网络理论

• 批准号: 2309896
• 负责人: Matteo Serafino
• 金额: $ 27.5万
• 依托单位: KCORE ANALYTICS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309896&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; Network

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. 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.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.

这项小型企业创新研究（SBIR）I期项目的更广泛/商业影响促进并提高了民主进程的透明度。它通过建立一个可以检测，理解和预测民主社会内意见趋势的社会意识体系来实现这一目标。通过开发尖端人工智能（AI）技术，该项目通过以高准确性来改善选举结果和社会意见趋势的预测来促进科学和技术知识。通过采用机器学习，该项目旨在超越传统投票方法的局限性，并为全球选举成果提供实时预测指标。该项目将解决有关社交媒体新闻的可信度，以增强人口的弹性，以免错误信息。此外，该项目通过积极寻求代表性不足的少数群体的参与来表明对包容性的承诺。这项小型企业创新研究（SBIR）I阶段项目旨在通过人工智能，网络理论和大数据科学的整合来实时预测全球选举。通过利用高级机器学习模型的力量并分析在社交媒体上公开表达的大量意见，该团队提供了准确的选举成果预测。这种方法有可能破坏常规的投票行业，该行业面临着不确定性和挑战，例如响应率下降和抽样中固有的偏见。研究目标需要应对批判性的研发和发展挑战，包括预测选民投票，有效地对农村地区进行有限的在线覆盖范围，过滤机器人和虚假新闻来源，推断出未经书面的选民的偏好，以逐步的基础来调整样本权重，以逐步宣传媒体的观点，以弥补社交态度，并散发出众多的态度，以弥补社交的意图（候选人的态度）（候选人的意图）（候选人均为综合性偏见）（候选人的态度）。预期的技术结果涉及开发基于图神经网络的变革性机器学习体系结构。 该框架可实现优化的资源分配，并显着提高预测的精度。最终，结果将使决策者有能力获得可靠的实时信息，促进知情选择并增强民主进程的韧性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。