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

人工智能（AI）为理解和预测复杂系统提供了强大的技术，例如建模和预测传染病的传播。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。阻碍它们被采用的一个问题是，众所周知，人工智能方法会放大它们所接受训练的数据中的偏见。这在传染病模型中尤其成问题，因为传染病模型利用了现有的大量且固有偏差的时空数据。这些偏见可能通过建模管道传播到决策，导致不公平和无效的政策干预。该项目研究了人工智能疾病建模管道如何从有偏见的数据转向有偏见的预测，并在三个目标中得出减轻这种偏见的解决方案：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，我们将从中收集数据，以一种控制偏差的方式，经常用作人工智能系统的输入；3)探索收集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成为可能，从而支持公平公正的决策和干预措施。该项目将丰富与人工智能伦理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专业发展机会。在Aim 1中，研究小组将使用一种自我监督的对比学习方法，将移动性预测作为文本前任务来学习空间区域的表征。这些表示可以用于传染病传播预测，只给定很少的传染病真实数据。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，研究团队将利用一个大规模的基于代理的模拟，作为一个我们拥有完美知识的沙盒世界，我们可以从中收集数据并注入各种类型的偏见。在目标3中，研究小组将通过利用人工智能中的不同公平指标，研究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指标纳入人工智能优化程序以减轻偏见。通过理解、衡量和减轻传统人工智能解决方案固有的偏见，该项目将实现准确、可扩展和快速的预测，以支持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之间的一个联合项目，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）的负责任和公平人工智能合作机会资助。该奖项反映了美国国家科学基金会的法定使命，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。

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.