III: Small: Bringing Transparency and Interpretability to Bias Mitigation Approaches in Place-based Mobility-centric Prediction Models for Decision Making in High-Stakes Settings

III：小：为基于地点的以移动性为中心的预测模型中的偏差缓解方法带来透明度和可解释性，以便在高风险环境中进行决策

• 批准号: 2210572
• 负责人: Vanessa Frias-Martinez
• 金额: $ 60万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210572&HistoricalAwards=false
• 关键词: III; Small; Bringing; Transparency; Interpretability

The covid-19 pandemic has brought to light the importance of place-based mobility-centric prediction models in high-stakes settings. Place-based mobility-centric prediction models (PBMC) use human mobility data - together with other contextual information - to predict spatio-temporal statistics of significance to decision makers. For example, mobility patterns that reflect (lack of) compliance with travel restrictions and stay-at-home orders have been used to predict the number of covid-19 cases over time. However, the data used to train PBMC models can suffer from different types of bias that might in turn affect the fairness of the predictions. For example, under-reporting in the covid-19 case data used to train PBMC models might produce predictions that are wrongfully low, which could lead a decision maker to, for example, not locate a covid-19 testing unit in a given neighborhood. This project presents a set of approaches to mitigate - in a transparent and interpretable manner - a diverse set of bias present in PBMC models for two high-stakes settings: public health and public safety. In addition, by providing insights into the processes that led to the embedding of bias in the data and into the effects of bias on the fairness of the models, this project will hopefully move PBMC models closer to broad adoption in policy settings. This project will also offer educational opportunities for graduate and undergraduate students as well as computing workshops for high school students and under-represented genders in computing with a focus on the value of PBMC models, human mobility data and fairness for high-stakes settings.The technical contributions of this project are divided in three thrusts. Thrust one will provide a novel PBMC prediction model - that can work with different neural architectures - to predict reported place-based statistics while mitigating for potential under-reporting bias. Thrust two will create a novel sampling bias mitigation approach to correct for under-represented groups in human mobility data collected from cell phones. Thrust three will produce novel transfer learning approaches to mitigate for algorithmic bias, i.e., low performing models in data-scarce regions. The thrusts proposed have been designed in a modular way, to allow for the layered combination of data and algorithmic bias mitigation approaches in end-to-end mitigation frameworks that are evaluated for fairness and accuracy. All bias mitigation methods are accompanied by novel interpretability approaches to distill the social determinants that might explain how the bias was embedded into place-based statistics and mobility data; as well as to identify the role that different model components might play in the mitigation itself. Our research outcomes will advance the state of the art in the design of transparent and interpretable bias mitigation approaches for PBMC models with evaluations in two high-stakes settings: public health and public safety.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.

2019冠状病毒病（COVID-19，即2019冠状病毒病）大流行突显了基于地点、以流动性为中心的预测模型在高风险环境中的重要性。基于地点的移动中心预测模型（PBMC）使用人类移动数据-以及其他上下文信息-来预测对决策者有意义的时空统计数据。例如，反映（不）遵守旅行限制和呆在家里的命令的流动模式已被用于预测一段时间内新冠肺炎病例的数量。然而，用于训练PBMC模型的数据可能会受到不同类型的偏差的影响，这反过来可能会影响预测的公平性。例如，用于训练PBMC模型的COVID-19病例数据的低报可能会产生错误的低预测，这可能导致决策者无法在给定的社区中找到COVID-19检测单位。该项目提出了一套方法，以透明和可解释的方式减轻PBMC模型中存在的两种高风险环境中的各种偏见：公共卫生和公共安全。此外，通过深入了解导致数据中嵌入偏见的过程以及偏见对模型公平性的影响，该项目有望使PBMC模型更接近于在政策环境中的广泛采用。该项目还将为研究生和本科生提供教育机会，并为高中生和在计算方面代表性不足的性别提供计算讲习班，重点是PBMC模型的价值、人类流动数据和高风险环境的公平性。Thrust One将提供一种新的PBMC预测模型-可以与不同的神经架构一起工作-以预测报告的基于位置的统计数据，同时减轻潜在的漏报偏倚。推力二将创建一种新的采样偏差缓解方法，以纠正从手机收集的人类移动数据中代表性不足的群体。推力三将产生新的迁移学习方法，以减轻算法偏差，即，数据稀缺地区的低性能模型。所提出的重点是以模块化方式设计的，以允许在端到端缓解框架中分层组合数据和算法偏差缓解方法，并对公平性和准确性进行评估。所有偏见缓解方法都伴随着新颖的可解释性方法，以提取可能解释偏见如何嵌入到基于地点的统计数据和流动性数据中的社会决定因素，并确定不同模型组件在缓解过程中可能发挥的作用。我们的研究成果将推动PBMC模型设计透明和可解释的偏倚缓解方法的最新技术水平，并在两个高风险环境中进行评估：公共卫生和公共安全。该奖项反映了NSF的法定使命，并被认为值得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来支持。