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III: Medium: Collaborative Research: Fairness in Web Database Applications

III: Medium: Collaborative Research: Fairness in Web Database Applications
III:媒介:协作研究:Web 数据库应用程序的公平性
批准号:
2106176
负责人:
Hosagrahar Jagadish
金额:
$29.2万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-11-01 至 2024-10-31

项目摘要

项目成果

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中文摘要
翻译
网络通过提供网络基础设施消除人与人之间的物理障碍,影响了人类生活和社会的各个角落。无数的网络数据库应用,包括在线推荐系统、在线购物网站、基于位置的网站、资源共享平台、社交媒体和服务网站,使人们的生活变得更加互联、便捷和经济高效。内部模型、算法、排名策略、用户分析和数据资源塑造了这些 Web 数据库应用程序的行为。 不幸的是,这些可能包括通过其服务、产品和建议传播甚至放大历史偏见的不公平做法。该项目旨在检测这种不公平现象,并在可能的情况下予以纠正。检测和纠正不公平现象的一个核心问题是,可以假设对这些网络系统的底层数据和算法有多少了解。 假设完全了解是不现实的,如果没有这样的假设,甚至很难发现不公平现象。 该项目依赖于有限的假设,例如后端数据库的存在。 在此基础上,该项目将检测无根据的偏见,开发可用于避免无意中的不公平现象的负责任的设计工具,并实施第三方工具来定制应对措施,以减少不同人口群体之间的差异。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。
英文摘要
The Web has affected every corner of human life and society by providing the cyber-infrastructure to remove physical barriers between people. Myriad web database applications, including online recommendation systems, online shopping sites, location-based websites, resource-sharing platforms, social media, and service websites, have made people's lives unimaginably more connected, convenient, and cost-effective. The internal models, algorithms, ranking strategies, user profiling, and data resources shape the behavior of these web database applications. Unfortunately, these can include unfair practices that propagate, or even amplify, historical biases through their services, products, and recommendations. This project aims to detect such unfairness and to correct it where possible.A central question in detecting and correcting unfairness is how much knowledge can be assumed about these web systems' underlying data and algorithms. It is unrealistic to assume full knowledge, and it is hard even to detect unfairness without such assumptions. This project relies on making limited assumptions, such as the existence of a back-end database. Based on these, the project will detect unwarranted bias, develop responsible design tools one can use to avoid inadvertent unfairness, and implement third-party tools to tailor responses to reduce disparities between different demographic groups.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.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
DENOUNCER: detection of unfairness in classifiers
DENOUNCER:检测分类器中的不公平性
DOI: 10.14778/3476311.3476328
发表时间: 2021
期刊: Proceedings of the VLDB Endowment
影响因子: 2.5
作者: [Li, Jinyang, Moskovitch, Yuval, Jagadish, H. V.]
通讯作者: Jagadish, H. V.
DOI: --
发表时间: 2021
期刊: IEEE Data Eng. Bull.
影响因子: --
作者: [Abolfazl Asudeh;You;Wu;Cong Yu;H. V. Jagadish]
通讯作者: Abolfazl Asudeh;You;Wu;Cong Yu;H. V. Jagadish
DOI: 10.1145/3514221.3522567
发表时间: 2022-06
期刊: Proceedings of the 2022 International Conference on Management of Data
影响因子: --
作者: [F. Nargesian;Abolfazl Asudeh;H. V. Jagadish]
通讯作者: F. Nargesian;Abolfazl Asudeh;H. V. Jagadish
Erica: Query Refinement for Diversity Constraint Satisfaction
Erica:多样性约束满足的查询细化
DOI: 10.14778/3611540.3611623
发表时间: 2023
期刊: Proceedings of the VLDB Endowment
影响因子: 2.5
作者: [Li, Jinyang, Silberstein, Alon, Moskovitch, Yuval, Stoyanovich, Julia, Jagadish, H. V.]
通讯作者: Jagadish, H. V.
9
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