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: 2302968
• 负责人: Andreas Zuefle
• 金额: $ 37.37万
• 依托单位: Emory 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=2302968&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。阻碍它们被采用的问题之一是，人工智能方法被认为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成问题，这些模型利用了可用的大型和固有偏差的时空数据。这些偏见可能会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引导到有偏见的预测，并在三个目标中得出缓解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）模拟一个人口，我们将从这个人口中收集数据，这些数据通常用作人工智能系统的输入，其方式是控制偏差，以及3）探索所收集数据中的偏差与人工智能模型中产生的偏差之间的联系，并得出缓解这些偏差的解决方案。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正的决策和干预措施。该项目将丰富与道德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专业发展机会。 在目标1中，研究团队将使用一种自我监督的对比学习方法，该方法使用移动性预测作为文本前任务来学习空间区域的表示。这些表示可以用于传染病传播预测，仅给出非常少的传染病地面实况数据。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，研究人员团队将利用一个大规模的基于代理的模拟，该模拟将作为一个沙箱世界，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见。对于目标3，研究团队将研究不同类型的模拟数据偏差如何通过利用人工智能中不同的公平性指标来导致有偏见的人工智能预测，并研究如何将这些公平性指标纳入人工智能优化过程以减轻偏差。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，和快速预测，以支持公平和公正的决定-这是美国和澳大利亚的研究人员之间的联合项目，由美国NSF和澳大利亚联邦科学和工业研究所资助的负责任和公平人工智能合作机会。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。

项目成果

Interpretation Attacks and Defenses on Predictive Models Using Electronic Health Records

• DOI: 10.1007/978-3-031-43418-1_27
• 发表时间: 2023
• 作者: Fereshteh Razmi;Jian Lou;Yuan Hong;Li Xiong

IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity

IGAMT：具有异构性和不规则性的隐私保护电子健康记录合成

• 发表时间: 2024
• 期刊: The 38th Annual AAAI Conference on Artificial Intelligence
• 作者: Wang, Wenjie;Tang, Pengfei;Lou, Jian;Shao, Yuanming;Waller, Lance;Ko, Yi-an;Xiong, Li
• 通讯作者: Xiong, Li