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: 2302969
• 负责人: Matthew Scotch
• 金额: $ 9.72万
• 依托单位: Arizona State 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=2302969&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。阻碍其采用的问题之一是，众所周知，人工智能方法会放大其训练数据中的偏差。这在利用现有的大量且固有偏差的时空数据的传染病模型中尤其成问题。这些偏见可能会通过建模渠道传播到决策，导致不公平和无效的政策干预。该项目研究人工智能疾病建模流程如何从有偏见的数据导致有偏见的预测，并得出缓解这种偏见的解决方案，以实现三个目标：1）创建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）模拟一个群体，我们将从中收集通常用作人工智能系统输入的数据，并以控制偏差的方式进行；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，并得出缓解其影响的解决方案。该项目将实现人工智能驱动的传染病模型和预测，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道德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。 在目标 1 中，研究人员团队将使用自我监督的对比学习方法，该方法使用移动性预测作为前置任务来学习空间区域的表示。仅在很少的传染病地面实况数据的情况下，这些表示可用于传染病传播预测。研究人员假设这种模型容易受到数据偏差的影响。因此，在目标 2 中，研究人员团队将利用大规模的基于代理的模拟，该模拟将作为一个沙盒世界，我们对此有完美的了解，并且可以从中收集数据并注入各种类型的偏差。对于目标 3，研究人员团队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差的人工智能预测，并研究如何将这些公平措施纳入人工智能优化程序中以减轻偏差。通过理解、测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，以支持公平公正的流行病预防决策。这是美国和澳大利亚研究人员之间的联合项目由美国 NSF 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，经评估认为值得支持基金会的智力价值和更广泛的影响审查标准。