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: 2107290
• 负责人: Abolfazl Asudeh
• 金额: $ 29.2万
• 依托单位: University of Illinois at Chicago
• 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
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107290&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Fairness

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.

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。该项目旨在检测此类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，开发负责任的设计工具，可用于避免无意中的不公平现象，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，并被视为值得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来支持。

项目成果

On Finding Rank Regret Representatives

论寻找排名遗憾代表

• DOI: 10.1145/3531054
• 发表时间: 2022-04
• 期刊: ACM Transactions on Database Systems
• 影响因子: 1.8
• 作者: Asudeh, Abolfazl;Das, Gautam;Jagadish, H. V.;Lu, Shangqi;Nazi, Azade;Tao, Yufei;Zhang, Nan;Zhao, Jianwen
• 通讯作者: Zhao, Jianwen

Next-generation Challenges of Responsible Data Integration

负责任的数据集成的下一代挑战

• DOI: 10.1145/3539597.3572727
• 发表时间: 2023-02
• 期刊: WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
• 作者: Nargesian, Fatemeh;Asudeh, Abolfazl;Jagadish, H. V.
• 通讯作者: Jagadish, H. V.

Responsible Data Integration: Next-generation Challenges

负责任的数据集成：下一代挑战

• DOI: 10.1145/3514221.3522567
• 发表时间: 2022-06
• 期刊: SIGMOD'22: Proceedings of the 2022 International Conference on Management of Data
• 作者: Nargesian, Fatemeh;Asudeh, Abolfazl;Jagadish, H. V.
• 通讯作者: Jagadish, H. V.

Data Coverage for Detecting Representation Bias in Image Datasets: A Crowdsourcing Approach

用于检测图像数据集中的表示偏差的数据覆盖率：一种众包方法

• DOI: 10.48550/arxiv.2306.13868
• 发表时间: 2023-06-24
• 作者: Melika Mousavi;N. Shahbazi;Abolfazl Asudeh
• 通讯作者: Abolfazl Asudeh

Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching

透过公平视角：实体匹配的实验分析与评估

• DOI: 10.14778/3611479.3611525
• 发表时间: 2023-07
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Shahbazi, Nima;Danevski, Nikola;Nargesian, Fatemeh;Asudeh, Abolfazl;Srivastava, Divesh
• 通讯作者: Srivastava, Divesh