Collaborative Research: Framework for Integrative Data Equity Systems

数据科学通过实现基于证据的决策，减少成本和错误，提高客观性，继续对科学和工程以及整个社会产生变革性影响。如果数据科学的技术和技术加剧不平等或泄露私人信息，它们也有巨大的危害潜力。因此，公共和私营部门的敏感数据集被限制用于研究，从而减缓了那些获益最大的领域的进展：公共部门的人类服务。此外，数据科学技术和技术的滥用将不成比例地伤害种族、性别、体能、性取向、教育等未被充分代表的群体。这些数据公平问题是普遍存在的，并且代表了在科学和工程中使用数据驱动方法的存在风险。该项目将建立一个综合数据公平系统框架（FIDES）：一个研究能够研究敏感数据同时防止滥用和误读的系统的研究所。FIDES将使跨学科社区围绕数据公平系统进行融合，在流动性、住房、教育、经济指标和政府透明度等关键领域进行初步研究，从而开发出一种支持综合数据科学责任的新型数据分析基础设施。为了实现这一目标，该项目将解决几个技术上具有挑战性的问题：(1)为了能够使用来自多个来源的数据，必须解决与隐私、偏见和滥用可能性相关的风险。该项目将开发数据集处理的原则方法来克服这些问题。(2)单个数据集难以集成以用于高级多层网络模型。该项目考虑了在空间和时间上一致的大型数据集上创建预训练张量的方法，使它们更容易合并，同时控制公平性和公平性。(3)任何数据集或模型都必须配备足够的信息来确定使用的适应性，传达局限性，并描述潜在的假设。该项目将开发工具和技术，为数据和模型制作“营养标签”，使专门用于公平问题的特别元数据来源方法正规化和标准化。除了支持数据科学的方法创新外，该研究所还将成为共享数据公平系统专业知识的焦点。它将通过建立数据科学和领域专家之间的交互接口来促进专业知识的开发和最佳实践的共享，并通过持续支持多样性和公平性的努力来实现这一目标。这个项目是美国国家科学基金会“利用数据革命大创意”活动的一部分。这项工作由高级网络基础设施办公室共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

Data Science continues to have a transformative impact on Science and Engineering, and on society at large, by enabling evidence-based decision making, reducing costs and errors, and improving objectivity. The techniques and technologies of data science also have enormous potential for harm if they reinforce inequity or leak private information. As a result, sensitive datasets in the public and private sector are restricted from research use, slowing progress in those areas that have the most to gain: human services in the public sector. Furthermore, the misuse of data science techniques and technologies will disproportionately harm underrepresented groups across race, gender, physical ability, sexual orientation, education, and more. These data equity issues are pervasive, and represent an existential risk for the use of data-driven methods in science and engineering. This project will establish a Framework for Integrative Data Equity Systems (FIDES): an Institute for the study of systems that enable research on sensitive data while preventing misuse and misinterpretation. FIDES will enable interdisciplinary community convergence around data equity systems, with an initial study in critical domains such as mobility, housing, education, economic indicators, and government transparency, leading to the development of a novel data analytics infrastructure that supports responsibility in integrative data science. Towards this goal, the project will address several technically challenging problems: (1) To be able to use data from multiple sources, risks related to privacy, bias, and the potential for misuse must be addressed. This project will develop principled methods for dataset processing to overcome these concerns. (2) Individual datasets are difficult to integrate for use in advanced multi-layer network models. This project considers methods to create pre-trained tensors over large collections of spatially and temporally coherent datasets, making them easier to incorporate while controlling for fairness and equity. (3) Any dataset or model must be equipped with sufficient information to determine fitness for use, communicate limitations, and describe underlying assumptions. This project will develop tools and techniques to produce "nutritional labels" for data and models, formalizing and standardizing ad hoc metadata approaches to provenance, specialized for equity issues. In addition to supporting methodological innovation in data science, the Institute will become a focal point for sharing expertise in data equity systems. It will do so by establishing interfaces for interaction between data science and domain experts to promote expertise development and sharing of best practices, and by consistently supporting efforts on diversity and equity.This project is part of the National Science Foundation's Harnessing the Data Revolution Big Idea activity. The effort is jointly funded by the Office of Advanced Cyberinfrastructure.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.