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CHS:Small: Incorporating and Balancing Stakeholder Values in Algorithm Design

CHS:Small：在算法设计中纳入并平衡利益相关者价值观

基本信息

批准号：
1908688
负责人：
Haiyi Zhu
金额：
$ 50万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2019-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908688&HistoricalAwards=false
关键词：
CHS Small Incorporating Balancing Stakeholder

项目摘要

This project will create a general method for value-sensitive algorithm design and develop tools and techniques to help incorporate the tacit values of stakeholders, balance multiple stakeholders' values, and achieve collective goals in the development of an algorithm. The research community has paid increasing attention to the role of human values in algorithm design and development. For example, fairness-aware machine learning research attempts to translate fairness notions into formal algorithmic constraints and develop algorithms subject to such constraints. Despite the mathematical rigor of these approaches, prior research suggests a disconnect between the current discrimination-aware machine learning research and stakeholders' realities, context, and constraints; this disconnect is likely to undermine practical initiatives. Furthermore, studies have suggested that there are often tensions among a diverse set of values relevant to the design of the algorithm. A new general method will be developed in the context of Redesigning Wikipedia's Objective Revision Evaluation Service (ORES), a machine learning-based service designed to generate real-time predictions on edit quality and article quality, which will benefit vast numbers of people who consume the Wikipedia content either directly or indirectly through other applications.There are four major goals of this research. The first is to articulate and demonstrate a general method for creating algorithmic systems that respect and balance stakeholders' values. The second goal is to create techniques for generating an algorithmic system's value report and explaining the value trade-offs. The third goal to create, deploy, and evaluate social and technical innovations to address fundamental trade-offs between different values. The final goal is to design and implement improvements to ORES, which will improve a wide variety of applications that rely on ORES, and Wikipedia's content and community as a whole. For an example of the kinds of problems that must be solved, quality control algorithms that prioritize efficiency in deleting low quality content incur the risk of undermining the motivation of contributors in peer production communities, particularly new contributors who are still learning how to contribute. To date, however, little work has been conducted to create solutions to address tensions and trade-offs between different values in algorithm design. The research will be performed through multiple studies by stepping through the process for a diverse set of tasks, each of which will allow interaction with multiple stakeholders, who have different (and perhaps conflicting) values.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.

该项目将创建一种对价值敏感的算法设计的通用方法，并开发工具和技术，以帮助纳入利益相关者的默契价值，平衡多个利益相关者的价值观，并在算法开发中实现集体目标。研究界越来越重视人的价值在算法设计和开发中的作用。例如，公平感知的机器学习研究试图将公平概念转换为形式算法约束，并开发受此类约束约束的算法。尽管这些方法在数学上很严谨，但先前的研究表明，当前具有歧视意识的机器学习研究与利益相关者的现实、背景和限制之间存在脱节；这种脱节可能会破坏实际行动。此外，研究表明，与算法设计相关的一组不同的值之间往往存在紧张关系。在重新设计维基百科的客观修订评估服务(ORES)的背景下，将开发一种新的通用方法。ORES是一种基于机器学习的服务，旨在生成对编辑质量和文章质量的实时预测，这将使广大通过其他应用程序直接或间接消费维基百科内容的人受益。第一个是阐明和演示创建尊重和平衡利益相关者价值观的算法系统的一般方法。第二个目标是创建用于生成算法系统的价值报告和解释价值权衡的技术。第三个目标是创造、部署和评估社会和技术创新，以解决不同价值观之间的根本权衡。最终目标是设计和实施对OreS的改进，这将改进依赖于OreS的各种应用程序，以及整个维基百科的内容和社区。举一个必须解决的问题的例子，在删除低质量内容时将效率放在首位的质量控制算法会导致破坏同行制作社区中投稿者的动机的风险，特别是仍在学习如何投稿的新投稿者。然而，到目前为止，还没有开展什么工作来创建解决方案，以解决算法设计中不同价值之间的紧张和权衡问题。这项研究将通过多项研究进行，逐步完成一系列不同的任务，每项任务都将允许与具有不同(甚至可能相互冲突)价值的多个利益相关者进行互动。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。