SHF: Medium: Fairness in Software Systems

软件在许多方面影响着社会，并日益使决策自动化。例如，软件转录视频，翻译文档，选择哪些新闻文章被推广，并决定谁获得贷款或被聘用。软件有可能在其操作中表现出偏见，无论它是否由软件的客户或开发人员有意为之。例如，软件在转录男性声音方面可能比女性声音更准确。或者，软件可能会向自动翻译注入社会刻板印象，风险评估计算可能会显示出种族偏见。随着越来越多的社会功能在网络空间中运行，软件公平的重要性也增加了。在这些环境中，数据驱动的软件有能力塑造人类的行为：它影响我们查看和购买的产品、我们阅读的新闻文章、我们参与的社交活动，并最终影响我们形成的观点。数据和软件中的偏见有可能在社会中形成、传播和永久存在偏见。该项目开发理论、技术和工具，使软件设计人员和工程师能够描述公平性需求，测试软件的公平性属性，并调试公平性缺陷。该项目的成果将有助于增加社会对软件决策和软件使用数据的信任，进而增加软件可以给社会带来的潜在影响和好处。该项目解决了高效和有效地衡量潜在偏见背后的科学问题，并帮助利益相关者做出关于软件的明智决策。该项目的目标不是制定政策或消除软件方面的偏见。相反，其目标是提供可针对正式指定的软件公平性属性进行验证的软件测试工具和措施。为了衡量偏差，该项目开发了一种新的方法来衡量程序输入和输出之间的因果关系。软件测试允许进行因果实验，包括使用几乎相同的输入运行软件，这些输入只在测试中的关键输入特性上有所不同。影响执行行为的输入特征的变化提供了因果关系的证据。该项目确定了什么时候因果关系适合于测量潜在的偏差，开发了测量这些关系的有效测试方法，并创建了工具和技术来帮助工程师识别和修改这些关系的原因。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.