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

新冠肺炎疫情暴露了基于地点的以流动为中心的预测模型在高风险环境中的重要性。基于地点的以移动为中心的预测模型(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具有重要意义的时空统计数据。例如，反映(不)遵守旅行限制和居家订单的流动模式已被用来预测随着时间的推移新冠肺炎案例的数量。然而，用于训练PBMC模型的数据可能会受到不同类型的偏差，这反过来可能会影响预测的公正性。例如，用于训练PBMC模型的新冠肺炎案例数据中的漏报可能会产生错误的低预测，这可能导致决策者例如没有在给定的社区找到新冠肺炎检测单位。该项目提出了一套方法，以透明和可解释的方式缓解PBMC模型中存在的针对两个高风险环境的各种偏见：公共卫生和公共安全。此外，通过提供对导致在数据中嵌入偏差的过程以及偏差对模型公平性的影响的洞察，该项目有望推动PBMC模型更接近于在政策环境中广泛采用。该项目还将为研究生和本科生提供教育机会，并为高中生和计算机代表不足的性别提供计算研讨会，重点关注PBMC模型的价值、人类流动性数据和高风险设置的公平性。该项目的技术贡献分为三个方面。推力一号将提供一种新的PBMC预测模型-可以与不同的神经体系结构一起工作-来预测已报告的基于地点的统计数据，同时减轻潜在的漏报偏差。推力二号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流动性数据中代表不足的群体。推力三将产生新的迁移学习方法，以缓解算法偏差，即数据稀缺地区的低性能模型。拟议的推进是以模块化方式设计的，以允许在端到端缓解框架中分层结合数据和算法偏差缓解方法，并对其进行公平和准确的评估。所有的偏差缓解方法都伴随着新颖的可解释性方法，以提取社会决定因素，这些社会决定因素可能解释偏差是如何嵌入到基于地点的统计数据和移动数据;中的，并确定不同的模型组件在缓解本身中可能扮演的角色。我们的研究成果将推动为PBMC模型设计透明和可解释的偏差缓解方法的最先进水平，并在两个高风险的环境中进行评估：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。

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.