SHF: Medium: Fairness in Software Systems

SHF：中：软件系统的公平性

• 批准号: 1763423
• 负责人: Yuriy Brun
• 金额: $ 105万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763423&HistoricalAwards=false
• 关键词: SHF; Medium; Fairness; Software; Systems

Software impacts society in many ways and increasingly automates decision-making. For example, software transcribes videos, translates documents, selects what news articles are promoted, and determines who gets a loan or gets hired. It is possible for software to exhibit bias in its operation, whether or not it is intended by the customers or developers of the software. For example, software might be more accurate at transcribing male voices than female ones. Or software may inject societal stereotypes into automated translations, and risk-assessment computations may exhibit racial bias. As more societal functions operate in cyberspace, the importance of software fairness increases. In these settings, data-driven software has the ability to shape human behavior: it affects the products we view and purchase, the news articles we read, the social interactions we engage in, and, ultimately, the opinions we form. Biases in data and software risk forming, propagating, and perpetuating biases in society. This project develops theory, techniques and tools to enable software designers and engineers to describe fairness requirements, test the software for fairness properties, and debug fairness defects. The outcomes of this project will help increase the society's trust in software decisions and in the data the software uses, in turn, increasing potential impact and benefits the software can bring to society.The project addresses scientific questions behind efficiently and effectively measuring potential bias and helping stakeholders make informed decisions about software. It is not the project's aim to devise policies or eliminate bias in software. Instead, the aim is to provide software testing tools and measures that can be validated for formally specified software fairness properties. To measure bias, the project develops a novel approach for measuring causal relationships between program inputs and outputs. Software testing enables conducting causal experiments consisting of running the software with nearly identical inputs that vary only in a key input characteristic under test. Variations in an input characteristic that affect execution behavior provide evidence of a causal relationship. The project identifies when causal relationships are appropriate for measuring potential bias, develops efficient testing methods for measuring these relationships, and creates tools and techniques to help engineers identify and modify the causes of these relationships.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.

软件以许多方式影响社会，并越来越自动化决策。例如，软件转录视频，翻译文档，选择了推广新闻文章，并确定谁获得贷款或被录用。无论是由软件的客户或开发人员打算，软件都有可能在其操作中表现出偏见。例如，与女性声音相比，软件在转录男性声音方面可能更准确。或软件可能会将社会刻板印象注入自动翻译中，并且风险评估计算可能表现出种族偏见。随着越来越多的社会功能在网络空间中运作，软件公平性的重要性也会提高。在这些环境中，数据驱动的软件具有塑造人类行为的能力：它影响我们查看和购买的产品，我们阅读的新闻文章，我们参与的社交互动以及最终形成的意见。数据和软件风险形成，传播和永久性偏见的偏见。该项目开发了理论，技术和工具，以使软件设计师和工程师能够描述公平要求，测试软件的公平性能以及调试公平性缺陷。该项目的结果将有助于提高社会对软件决策的信任，并在软件中使用该软件可以为社会带来的潜在影响和利益的数据。该项目解决了有效有效地衡量潜在偏见的科学问题，并帮助利益相关者做出有关软件的知情决策。该项目的目的不是制定政策或消除软件中的偏见。相反，目的是提供软件测试工具和措施，这些工具和措施可以为正式指定的软件公平属性进行验证。为了衡量偏见，该项目开发了一种新的方法，用于测量程序输入和产出之间的因果关系。软件测试使进行因果实验，该因果实验包括运行软件，几乎相同的输入仅在测试的关键输入特征中有所不同。影响执行行为的输入特征的变化提供了因果关系的证据。该项目确定何时因果关系适合衡量潜在的偏见，开发有效的测试方法来衡量这些关系，并创建工具和技术，以帮助工程师识别和修改这些关系的原因。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛影响来评估NSF的法定任务，并被认为是值得的。

项目成果

CoCo: Interactive Exploration of Conformance Constraints for Data Understanding and Data Cleaning

CoCo：数据理解和数据清理的一致性约束的交互式探索

• DOI: 10.1145/3448016.3452750
• 发表时间: 2021
• 期刊: International Conference on Management of Data (SIGMOD
• 作者: Fariha, Anna;Tiwari, Ashish;Meliou, Alexandra;Radhakrishna, Arjun;Gulwani, Sumit
• 通讯作者: Gulwani, Sumit

Explain 3D: explaining disagreements in disjoint datasets

解释 3D：解释不相交数据集中的分歧

• DOI: 10.14778/3317315.3317320
• 发表时间: 2019
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Wang, Xiaolan;Meliou, Alexandra
• 通讯作者: Meliou, Alexandra

Fairkit-learn: A Fairness Evaluation and Comparison Toolkit

Fairkit-learn：公平性评估和比较工具包

• DOI: 10.1145/3510454.3516830
• 发表时间: 2022
• 期刊: Proceedings of the Demonstrations Track at the 44th International Conference on Software Engineering (ICSE
• 作者: Johnson, Brittany;Brun, Yuriy
• 通讯作者: Brun, Yuriy

Software Fairness

• DOI: 10.1145/3236024.3264838
• 发表时间: 2018-01-01
• 期刊: ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING
• 作者: Brun, Yuriy;Meliou, Alexandra
• 通讯作者: Meliou, Alexandra

Improved Approximation and Scalability for Fair Max-Min Diversification

• DOI: 10.4230/lipics.icdt.2022.7
• 发表时间: 2022-01
• 期刊: Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases
• 作者: Raghavendra Addanki;A. Mcgregor;A. Meliou;Zafeiria Moumoulidou