Collaborative Research: Framework for Integrative Data Equity Systems

协作研究：综合数据公平系统框架

• 批准号: 1934464
• 负责人: Julia Stoyanovich
• 金额: $ 55万
• 依托单位: New York University
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934464&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Integrative; Data

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，降低成本和错误以及提高客观性的方式，对科学和工程以及整个社会产生变革性的影响。数据科学的技术和技术在加强不平等或泄漏私人信息的情况下也具有巨大的伤害潜力。 结果，公共和私营部门的敏感数据集受到研究的限制，在那些拥有最大收益的领域的进度放缓：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损害种族，性别，身体能力，性取向，教育等方面代表性不足的群体。这些数据权益问题普遍存在，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股权系统（FIDE）的框架：一个系统研究所，可在防止滥用和误解的同时研究敏感数据。 FIDE将在数据股权系统周围实现跨学科的社区融合，并在关键领域（例如移动性，住房，教育，经济指标和政府透明度）进行了最初的研究，从而发展了新的数据分析基础架构，以支持整合数据科学的责任。 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）必须解决来自多个来源的数据，与隐私，偏见有关的风险，并且必须解决滥用的潜力。该项目将开发用于克服这些问题的数据集处理的原则方法。 （2）很难集成单个数据集，以用于高级多层网络模型。 该项目考虑了在空间和时间相干数据集中创建预训练的张量的方法，从而使它们更易于在控制公平和公平性的同时合并。 （3）任何数据集或模型都必须配备足够的信息，以确定使用的适合度，交流限制并描述基本假设。 该项目将开发工具和技术，以生产数据和模型的“营养标签”，将临时元数据的正式化和标准化来源的临时方法，专门针对股权问题。除了支持数据科学的方法论创新外，该研究所还将成为共享数据股权系统专业知识的重点。 它将通过建立数据科学与领域专家之间互动的界面来促进专业知识发展和最佳实践的共享，并始终如一地支持在多样性和公平上的努力。该项目是国家科学基金会刺激数据革命的大型思想活动的一部分。 这项努力由高级网络基础设施办公室共同资助。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估的。

项目成果

Causal Intersectionality and Fair Ranking

因果交叉性和公平排名

• 发表时间: 2021
• 期刊: 2nd Symposium on Foundations of Responsible Computing (FORC
• 作者: Yang, Ke;Loftus, Joshua R.;Stoyanovich, Julia
• 通讯作者: Stoyanovich, Julia

Data distribution debugging in machine learning pipelines

• DOI: 10.1007/s00778-021-00726-w
• 发表时间: 2022-01
• 期刊: The VLDB Journal
• 作者: Stefan Grafberger;Paul Groth;Julia Stoyanovich;Sebastian Schelter

Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences

最受期待的获胜者：对不确定选民偏好的获胜者的解读

• 发表时间: 2023
• 期刊: Proceedings of the '23 International Conference on the Management of Data (ACM SIGMOD 2023
• 作者: Haoyue Ping;Julia Stoyanovich
• 通讯作者: Julia Stoyanovich

Taming Technical Bias in Machine Learning Pipelines

克服机器学习管道中的技术偏见

• 发表时间: 2020
• 期刊: Bulletin of the Technical Committee on Data Engineering
• 作者: Schelter, Sebastian;Stoyanovich, Julia
• 通讯作者: Stoyanovich, Julia

Counterfactuals for the Future

未来的反事实

• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Bynum, Lucius;Loftus, Joshua;Stoyanovich, Julia
• 通讯作者: Stoyanovich, Julia