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来消除人与人之间的物理障碍，影响了人类生活和社会的每一个角落。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在内的无数网络数据库应用，使人们的生活无可争议地更加互联、便捷和划算。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，这些可能包括不公平的做法，通过他们的服务，产品和建议传播甚至放大历史偏见。该项目旨在检测这种不公平并在可能的情况下纠正它。检测和纠正不公平的一个中心问题是可以假设这些Web系统的底层数据和算法有多少知识。 假设完全知情是不现实的，如果没有这样的假设，甚至很难发现不公平。 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，该项目将检测不必要的偏见，开发负责任的设计工具，可以用来避免无意中的不公平，并实施第三方工具，以定制响应，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，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Fair active learning

公平主动学习

• DOI: 10.1016/j.eswa.2022.116981
• 发表时间: 2022
• 期刊: Expert Systems with Applications
• 影响因子: 8.5
• 作者: Anahideh, Hadis;Asudeh, Abolfazl;Thirumuruganathan, Saravanan
• 通讯作者: Thirumuruganathan, Saravanan

Fairness-Aware Range Queries for Selecting Unbiased Data

用于选择无偏数据的公平感知范围查询

• DOI: 10.1109/icde53745.2022.00111
• 发表时间: 2022
• 期刊: ICDE'22: IEEE international conference on Data Engineering
• 作者: Shetiya, Suraj;Swift, Ian P.;Asudeh, Abolfazl;Das, Gautam
• 通讯作者: Das, Gautam

Representation Bias in Data: A Survey on Identification and Resolution Techniques

• DOI: 10.1145/3588433
• 发表时间: 2022-03
• 期刊: ACM Computing Surveys
• 影响因子: 16.6
• 作者: N. Shahbazi;Yin Lin;Abolfazl Asudeh;H. V. Jagadish

Maximizing Neutrality in News Ordering

• DOI: 10.1145/3580305.3599425
• 发表时间: 2023-05
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Rishi Advani;Paolo Papotti;Abolfazl Asudeh

On Finding Rank Regret Representatives

论寻找排名遗憾代表

• DOI: 10.1145/3531054
• 发表时间: 2022
• 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