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.

covid-19 大流行凸显了基于地点的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以流动性为中心的预测模型 (PBMC) 使用人员流动性数据以及其他上下文信息来进行预测。例如，反映（缺乏）遵守旅行限制和居家令的流动模式已被用来预测一段时间内的 covid-19 病例数量。用于训练的数据PBMC 模型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 病例数据的漏报可能会产生错误的低预测，这可能会导致错误的结果。例如，决策者不想在给定的社区中找到 covid-19 测试单位，该项目提出了一套方法，以透明且可解释的方式减轻 PBMC 模型中存在的多种偏见。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，该项目有望推动 PBMC 模型的发展。该项目还将为研究生和本科生提供教育机会，并为高中生和计算领域代表性不足的性别提供计算研讨会，重点关注 PBMC 模型、人口流动数据和计算的价值。公平为该项目的技术贡献分为三个主旨，其中一个主旨将提供一种新颖的 PBMC 预测模型，可以与不同的神经架构一起工作，以预测报告的基于地点的统计数据，同时减轻潜在的漏报偏差。推力二将创建一种新颖的采样偏差缓解方法，以纠正从手机收集的人员流动数据中代表性不足的群体。推力三将产生新颖的迁移学习方法，以减轻算法偏差，即低性能模型。所提出的主旨以模块化方式设计，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，并评估所有偏差缓解方法的公平性和准确性。伴随着新颖的可解释性方法，可以提取社会决定因素，这些因素可以解释偏见是如何嵌入到基于地点的统计数据和流动性数据中的，并确定不同模型组件在缓解本身中可能发挥的作用；透明设计的最先进水平PBMC 模型的可解释偏差缓解方法，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，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。