SBIR Phase I: Artificial Intelligence and Network Theory for Elections

这一小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响促进和提高民主进程的透明度。它通过开发一个社会意识系统来实现这一目标，该系统可以检测，理解和预测民主社会中的舆论趋势。该项目通过开发尖端的人工智能（AI）技术，以高精度提高对选举结果和社会舆论趋势的预测，从而为科学技术知识做出贡献。通过使用机器学习，该项目旨在超越传统投票方法的局限性，并提供全球选举结果的实时预测。该项目将解决社交媒体上新闻的可信度问题，以加强民众对错误信息的抵御能力。此外，该项目通过积极寻求代表性不足的少数群体的参与，展示了对包容性的承诺。这个小企业创新研究（SBIR）第一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。通过利用先进的机器学习模型的力量，并分析社交媒体上公开表达的大量意见，该团队提供了对选举结果的准确预测。这种方法有可能破坏传统的民意调查行业，该行业面临着越来越多的不确定性和挑战，例如答复率下降和抽样中的固有偏见。研究目标需要解决关键的研究和开发挑战，包括预测选民投票率，有效地对在线覆盖有限的农村地区进行抽样，过滤机器人和假新闻来源，推断尚未决定的选民的偏好，调整样本权重在各州的基础上，解决在社交媒体上不活跃的个人的意见，减轻社会期望偏差（受访者隐瞒他们投票给有争议的候选人的意图）。预期的技术成果涉及开发基于图神经网络的变革性机器学习架构。 该框架能够优化资源分配，并显着提高预测的精度。最终，研究结果将为决策者提供可靠的实时信息，促进知情选择，并增强民主进程的弹性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.