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Collaborative Research: Framework for Integrative Data Equity Systems

协作研究：综合数据公平系统框架

基本信息

批准号：
1934565
负责人：
Hosagrahar Jagadish
金额：
$ 76.23万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934565&HistoricalAwards=false
关键词：
Collaborative Research Framework Integrative Data

项目摘要

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.

数据科学通过实现基于证据的决策、减少成本和错误以及提高客观性，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方法加剧不平等或泄露私人信息，它们也有巨大的潜在危害。因此，公共和私营部门的敏感数据集被限制用于研究，减缓了那些最有可能获益的领域的进展：公共部门的人类服务。此外，数据科学技术的滥用将对种族、性别、体能、性取向、教育等代表性不足的群体造成不成比例的伤害。这些数据公平问题是普遍存在的，并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）：一个研究系统的研究所，该系统能够对敏感数据进行研究，同时防止滥用和误解。FIDES将围绕数据公平系统实现跨学科社区融合，在关键领域进行初步研究，如移动性，住房，教育，经济指标和政府透明度，从而开发一种新型的数据分析基础设施，支持综合数据科学的责任。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、偏见和滥用可能性有关的风险。该项目将开发数据集处理的原则性方法，以克服这些问题。 (2)单个数据集很难集成到高级多层网络模型中使用。该项目考虑在大量空间和时间相干数据集上创建预训练张量的方法，使它们更容易合并，同时控制公平和公正。 (3)任何数据集或模型都必须配备足够的信息，以确定是否适合使用，传达限制并描述基本假设。该项目将开发工具和技术，为数据和模型制作“营养标签”，使专门处理公平问题的来源特设元数据方法正规化和标准化。除了支持数据科学的方法创新外，研究所还将成为分享数据公平系统专门知识的协调中心。它将通过建立数据科学和领域专家之间的互动接口来实现这一目标，以促进专业知识的发展和最佳实践的共享，并始终如一地支持多样性和公平方面的努力。该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。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

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Patterns Count-Based Labels for Datasets

数据集基于计数的模式标签

DOI：
10.1109/icde51399.2021.00184
发表时间：
2021
期刊：
{ICDE}
影响因子：
0
作者：
Moskovitch, Yuval;Jagadish, H. V.
通讯作者：
Jagadish, H. V.

COUNTATA: Dataset Labeling Using Pattern Counts

COUNTATA：使用模式计数的数据集标记

DOI：
发表时间：
2020
期刊：
Proceedings of the VLDB Endowment
影响因子：
2.5
作者：
Moskovitch, Yuval;Jagadish, H. V.
通讯作者：
Jagadish, H. V.

Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes

识别序数连续值属性的数据覆盖不足

DOI：
10.1145/3448016.3457315
发表时间：
2021
期刊：
ACM International Conference on Management of Data {SIGMOD}
影响因子：
0
作者：
Asudeh, Abolfazl;Shahbazi, Nima;Jin, Zhongjun;Jagadish, H. V.
通讯作者：
Jagadish, H. V.

COVID-19 Brings Data Equity Challenges to the Fore

COVID-19 使数据公平性挑战凸显

DOI：
10.1145/3440889
发表时间：
2021
期刊：
Digital Government: Research and Practice
影响因子：
0
作者：
Jagadish, H. V.;Stoyanovich, Julia;Howe, Bill
通讯作者：
Howe, Bill

MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness

MithraCoverage：用于调查群体偏差以实现交叉公平的系统

DOI：
10.1145/3318464.3384689
发表时间：
2020
期刊：
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
影响因子：
0
作者：
Jin, Zhongjun;Xu, Mengjing;Sun, Chenkai;Asudeh, Abolfazl;Jagadish, H. V.
通讯作者：
Jagadish, H. V.