EAGER: Collaborative Research: Toward Informing Users About Algorithmic Fairness

EAGER：协作研究：向用户通报算法公平性

• 批准号: 1844518
• 负责人: Michael Tschantz
• 金额: $ 5.22万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844518&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Toward; Informing; EAGER; Collaborative; Research; Toward; Informing

Computers make important decisions about people, including about criminal justice issues such as sentencing and bail. These decisions can sometimes be considered discriminatory if the computer system does not treat people -- for example, people of different races -- fairly. However, deciding what it means for a computer system to be "fair" is complicated: there are many possible mathematical definitions of fairness, and a system cannot achieve them all at the same time. For society to make policy related to these definitions of fairness, non-technical people -- from legal and policy experts to the general public -- must be able to understand subtle distinctions between mathematical concepts. This research will develop and evaluate approaches to explaining these concepts to non-experts, so that future research can investigate people's opinions about them. The proposed work will develop and evaluate text and graphical descriptions and/or vignettes illustrating different nondiscrimination properties and their tradeoffs. For concreteness, in this exploratory work the project will focus only on accuracy-like nondiscrimination properties, only in the context of criminal justice, such as algorithms used in bail and sentencing decisions. The project will use iterative, qualitative, person-centered design, including interviews and co-design studies with both non-computer-science subject-matter experts in law and social science and laypeople to develop and preliminarily evaluate the explanations. In parallel, the project will systematize the space of nondiscrimination properties. This effort will inform qualitative design efforts; concurrently, interviews with legal and ethical experts will also shape the systematization, in a process of iterative refinement. The end product will be a description of how various nondiscrimination definitions differ along the axes empirical studies find most important.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.

计算机就人们做出重要决定，包括关于刑事司法问题，例如量刑和保释。如果计算机系统不对人（例如，不同种族的人）公平地对待人，这些决定有时可以视为歧视性。但是，确定计算机系统“公平”的含义是复杂的：有许多可能的数学定义，并且系统无法同时实现所有这些定义。为了使社会制定与这些公平定义有关的政策，从法律和政策专家到公众的非技术人员必须能够理解数学概念之间的微妙区别。这项研究将开发和评估向非专家解释这些概念的方法，以便未来的研究可以调查人们对它们的看法。拟议的工作将开发和评估文本和图形描述和/或小插曲，以说明不同的非歧视属性及其权衡。为了具体，在这项探索性工作中，该项目仅在刑事司法的背景下，例如在保释和判刑决定中使用的算法。该项目将使用迭代性，定性，以人为本的设计，包括与非科学科学和社会科学专家和外行人的访谈和共同设计研究，以开发和初步评估这些解释。同时，该项目将系统化非歧视性能的空间。 这项工作将为定性设计工作提供依据；同时，与法律和道德专家的访谈也将在迭代精致过程中塑造系统化。 最终产品将描述各种非歧视定义如何沿轴实证研究发现最重要的不同。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。