Collaborative Research: NSF-CSIRO: HCC: Small: Understanding Bias in AI Models for the Prediction of Infectious Disease Spread

合作研究：NSF-CSIRO：HCC：小型：了解预测传染病传播的 AI 模型中的偏差

• 批准号: 2302970
• 负责人: Taylor Anderson
• 金额: $ 12.39万
• 依托单位: George Mason University
• 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: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302970&HistoricalAwards=false
• 关键词: Collaborative; Research; NSF; CSIRO; HCC

Artificial intelligence (AI) provides powerful techniques for understanding and prediction of complex systems such as modeling and predicting the spread of infectious diseases. Despite this, these predictive capabilities are rarely adopted by public health decision-makers to support policy interventions. One of the issues preventing their adoption is that AI methods are known to amplify the bias in the data they are trained on. This is especially problematic in infectious disease models which leverage available large and inherently biased spatiotemporal data. These biases may propagate through the modeling pipeline to decision-making, resulting in inequitable and ineffective policy interventions. This project investigates how the AI disease modeling pipeline can lead from biased data to biased predictions and to derive solutions that mitigate this bias in three aims: 1) creating an AI system to predict the spread of emerging infectious diseases in space and time, 2) simulating a population from which we will collect data often used as input for AI systems in a way that the bias is controlled, and 3) exploring links between bias in the collected data and the resulting bias in the AI model and deriving solutions for their mitigation. The project will enable AI-driven infectious disease models and predictions that will support fair and equitable decision-making and interventions. The project will enrich education and training related to ethical AI practices and will support professional development opportunities for early-career researchers, graduate, undergraduate, and high school students in the United States and Australia. In Aim 1, the team of researchers will use a self-supervised contrastive learning approach that uses mobility prediction as a pre-text task to learn representations of spatial regions. These representations can be used for infectious disease spread prediction given only very little infectious disease ground truth data. The investigators hypothesize that such a model is susceptible to data bias. Thus, in Aim 2, the team of researchers will leverage a large-scale agent-based simulation that will serve as a sandbox world for which we have perfect knowledge of and from which we can collect data and inject various types of bias. For Aim 3, the team of researchers will investigate how different types of simulated data bias leads to biased AI predictions by leveraging different metrics of fairness in AI and studying how these fairness measures can be incorporated into the AI optimization procedure to mitigate bias. By understanding, measuring, and mitigating bias inherent to traditional AI solutions, the project will enable accurate, scalable, and rapid predictions to support fair and equitable decision-making for pandemic prevention.This is a joint project between researchers in the United States and Australia funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organization (CSIRO).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.

人工智能(AI)为理解和预测复杂系统提供了强大的技术，例如对传染病的传播进行建模和预测。尽管如此，公共卫生决策者很少采用这些预测能力来支持政策干预。阻碍它们被采用的问题之一是，众所周知，人工智能方法会放大它们所训练的数据中的偏差。这在传染病模型中尤其有问题，因为它利用了可用的大量和固有的有偏见的时空数据。这些偏见可能会通过建模渠道传播到决策过程，导致不公平和无效的政策干预。这个项目调查了人工智能疾病建模管道如何将有偏差的数据引向有偏差的预测，并从三个目标中推导出缓解这种偏差的解决方案：1)创建一个人工智能系统来预测新出现的传染病在空间和时间上的传播，2)模拟一个人群，我们将从其中收集经常用作人工智能系统输入的数据，以一种偏差受到控制的方式，以及3)探索收集的数据中的偏差与人工智能模型中由此产生的偏差之间的联系，并得出缓解它们的解决方案。该项目将使人工智能驱动的传染病模型和预测能够支持公平和公平的决策和干预措施。该项目将丰富与伦理人工智能实践相关的教育和培训，并将为美国和澳大利亚的早期职业研究人员、研究生、本科生和高中生提供职业发展机会。在目标1中，研究小组将使用一种自我监督的对比学习方法，将流动性预测作为前文任务来学习空间区域的表示。在传染病地面真实数据很少的情况下，这些表示法可用于传染病传播预测。研究人员假设，这样的模型容易受到数据偏差的影响。因此，在目标2中，研究团队将利用基于代理的大规模模拟作为沙盒世界，我们可以从沙盒世界收集数据并注入各种类型的偏见。对于目标3，研究团队将通过利用人工智能中不同的公平衡量标准，调查不同类型的模拟数据偏差如何导致有偏见的人工智能预测，并研究如何将这些公平衡量标准纳入人工智能优化程序以减轻偏差。通过了解、测量和减轻传统人工智能解决方案固有的偏差，该项目将实现准确、可扩展和快速的预测，以支持针对大流行预防的公平和公正决策。这是美国和澳大利亚研究人员的联合项目，由美国国家科学基金会和澳大利亚联邦科学与工业研究组织(CSIRO)下的负责任和公平人工智能合作机会资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。