RAPID: Engaging High School Youth in Algorithmic Justice Through Audits of Designed and Everyday Machine Learning Applications

人工智能（AI）和机器学习（ML）应用的快速发展导致全国范围内呼吁支持青年发展人工智能素养，有效地与人工智能互动并批判性地评估人工智能所需的能力。最重要的是，必须让更多的年轻人了解这些技术是如何工作的，它们对个人和社会的影响，以及它们如何增加或破坏公平。迫切需要研究年轻人在日常生活中对人工智能的理解，例如与语音助手或社交媒体应用程序的互动。在这个项目中，高中生将学习人工智能和机器学习，重点是算法审计的概念，这是一种通过反复查询人工智能系统来理解人工智能算法不透明的内部工作原理的方法，以解释其外部效果和影响。这项建议是在回复致同事信（DCL）后收到的：“正式和非正式环境下K-12教育中快速加速人工智能研究（NSF 23-097）”，由学生和教师创新技术体验（ITEST）项目资助，该项目支持建立对实践、项目要素、背景和过程的理解，有助于增加学生对科学、技术、工程的知识和兴趣的项目。数学（STEM）和信息与通信技术（ICT）职业。研究人员将开展一项为期一年的研究，让高中生为他们的同龄人设计日常机器学习应用程序，并协同进行算法审计，重点是算法的公平性、问责制和公正性。以下研究问题将被检验：(1)高中生目前对日常机器学习应用的经验和理解是什么？(2)高中生如何设计和实施机器学习应用的协同审计？(3)高中生如何将算法审计方法应用于他们日常生活中遇到的应用程序？为了回答这些研究问题，来自费城的不同高中青年团体，包括黑人，西班牙裔和拉丁裔年轻人，将在团队中工作，并参加扩展的研讨会，设计和协作审核各种带有文本，声音和图像的机器学习应用程序。通过联合设计、访谈和观察方法，研究人员将深入了解算法审计的可行性，这些年轻人是这些系统最常见的用户；生产终端用户算法审计的协作交互动态；以及年轻人通过审计对算法公正的理解。以前的最终用户算法审计研究只关注非专家成年人。获得的关于年轻人算法审计方法的见解将产生新的知识，并有可能为其他人工智能和机器学习扫盲工作提供信息。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Rapid developments in artificial intelligence (AI) and machine learning (ML) applications have led to nation-wide calls for supporting youth in the development of artificial intelligence literacy, competencies needed to effectively interact with and critically evaluate artificial intelligence. Most importantly, a broader range of youths must be equipped to understand how these technologies work, their personal and social impacts, and how they may increase or undermine equity. There is an urgent need for research on youth's understanding of AI in everyday contexts that permeate their daily lives, such as interaction with voice assistants or social media applications. In this project high school-aged youth will learn artificial intelligence and machine learning with a focus on the concept of algorithm auditing, a method for understanding an AI algorithm's opaque inner workings by repeatedly querying the AI system in order to interpret its external effects and impacts. This proposal was received in response to the Dear Colleague Letter (DCL): "Rapidly Accelerating Research on Artificial Intelligence in K-12 Education in Formal and Informal Settings (NSF 23-097)" and funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.Researchers will carry out a one-year study involving high school youth designing everyday machine learning applications for their peers, and collaboratively conducting algorithm audits with a focus on algorithm fairness, accountability, and justice. The following research questions will be examined: (1) What are high school youths' current experiences and understandings of everyday machine learning applications? (2) How do high school youth design and conduct collaborative audits of machine learning applications? And (3) How can high school youth apply algorithm auditing approaches to applications they encounter in their everyday lives? To answer these research questions, groups of diverse high school youth from Philadelphia, including Black, Hispanics and Latinos young people, will work in teams and participate in extended workshops to design and collaboratively audit a variety of machine learning applications with text, sound, and images. Using a combination of co-design, interviews, and observational methods, researchers will gain insights into the feasibility of algorithm audits by youth who are some of these systems' most common users; the dynamics of collaborative interactions for productive end-user algorithm audits; and youth understandings of algorithmic justice through auditing. Previous end-user algorithm auditing research has focused only on non-expert adults. The insights gained about youth's approaches to algorithm audits will generate new knowledge, and also have the potential to inform other artificial intelligence and machine learning literacy efforts.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.