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CAREER: Participatory Design Methods for Algorithmic Systems

职业：算法系统的参与式设计方法

基本信息

批准号：
1844901
负责人：
Eric Baumer
金额：
$ 54.99万
依托单位：
Lehigh University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844901&HistoricalAwards=false
关键词：
CAREER Participatory Design Methods Algorithmic

项目摘要

This research will develop methods for applying participatory design to the underlying components of algorithmic systems. Such systems incorporate increasingly complex algorithms from machine learning (ML), natural language processing (NLP), and related areas into user interactions, intending to achieve great benefits. However, cases of egregious errors, algorithmic bias, and other issues have revealed the shortcomings of relying primarily on quantitative performance metrics to inform design. A potential solution is to incorporate users into the design process. Although human-computer interaction research has developed numerous methods for user-centered design, many such approaches focus primarily at the interface level. This focus becomes problematic when a system's functionality is increasingly determined by ML models and algorithms. Furthermore, designing only for users of algorithmic systems can overlook other important relationships, such as the people whose data are being analyzed or those who may interpret the results. To address these issues, this research will develop participatory methods for human-centered design of algorithmic systems. These methods will be developed and tested by working closely with two non-profit organizations that already engage in data-intensive work but currently make limited use of algorithmic systems: AEquitas, which conducts legal analysis, and ProPublica, an investigative journalism newsroom. Unique challenges emerge when attempting to incorporate different people's relationships with a system into the design process. Existing participatory methods often use visual elements or manipulatives to represent interface components. The abstract mathematical formalisms of algorithmic systems, though, do not always lend themselves to such visual representations. This research will develop novel participatory design techniques to establish common ground between domain experts, who are less familiar with ML or NLP, and researchers, who are unfamiliar with the application domain. Doing so can leverage diverse participants' expertise and interpretations, thereby improving the fit between computational systems and existing practices. The participatory methods directly address underlying technical components, from feature selection, to model construction, to performance evaluation, to result interpretation. Furthermore, these methods will align those underlying technical aspects with current practices and lay understandings, increase the chance of catching and rectifying unanticipated egregious errors before they become problematic, and ensure the results are presented in a transparent and accountable manner. Finally, this process will inform the development of modules for classroom instruction, paired across STEM and social science courses, on how to incorporate human-centered concerns into designing algorithmic systems.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.

这项研究将开发的方法应用参与式设计的算法系统的基本组成部分。这些系统将机器学习（ML），自然语言处理（NLP）和相关领域的日益复杂的算法融入用户交互中，旨在实现巨大的利益。然而，令人震惊的错误，算法偏差和其他问题的情况下，揭示了主要依赖于定量性能指标来通知设计的缺点。一个潜在的解决方案是将用户纳入设计过程。虽然人机交互研究已经开发了许多以用户为中心的设计方法，但许多这样的方法主要集中在界面层面。当系统的功能越来越多地由ML模型和算法决定时，这种关注就成了问题。此外，只为算法系统的用户设计可能会忽略其他重要的关系，例如数据被分析的人或可能解释结果的人。为了解决这些问题，本研究将开发以人为本的算法系统设计的参与式方法。这些方法将通过与两个非营利组织密切合作来开发和测试，这两个组织已经从事数据密集型工作，但目前对算法系统的使用有限：进行法律的分析的AEquitas和调查性新闻编辑室ProPublica。当试图将不同的人与系统的关系整合到设计过程中时，会出现独特的挑战。现有的参与式方法通常使用视觉元素或操纵来表示界面组件。然而，算法系统的抽象数学形式并不总是适合这种视觉表示。这项研究将开发新的参与式设计技术，以建立领域专家之间的共同点，谁是不熟悉ML或NLP，和研究人员，谁是不熟悉的应用领域。这样做可以利用不同参与者的专业知识和解释，从而提高计算系统和现有实践之间的契合度。参与式方法直接涉及基本的技术组成部分，从特征选择到模型构建，到绩效评估，再到结果解释。此外，这些方法将使这些基本的技术方面与当前的实践和理解保持一致，增加在出现问题之前发现和纠正意外的严重错误的机会，并确保以透明和负责任的方式呈现结果。最后，这个过程将为STEM和社会科学课程的课堂教学模块的开发提供信息，指导如何将以人为本的关注纳入算法系统的设计中。该奖项反映了NSF的法定使命，并被认为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来支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Topicalizer: reframing core concepts in machine learning visualization by co-designing for interpretivist scholarship

Topicalizer：通过解释主义学术的共同设计重新构建机器学习可视化的核心概念

DOI：
10.1080/07370024.2020.1734460
发表时间：
2020
期刊：
Human–Computer Interaction
影响因子：
0
作者：
Baumer, Eric P.;Siedel, Drew;McDonnell, Lena;Zhong, Jiayun;Sittikul, Patricia;McGee, Micki
通讯作者：
McGee, Micki

Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism

故事从何而来？