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

RAPID：通过审核设计的和日常的机器学习应用程序让高中青年参与算法正义

• 批准号: 2333469
• 负责人: Yasmin Kafai
• 金额: $ 20万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333469&HistoricalAwards=false
• 关键词: RAPID; Engaging; Youth; Algorithmic; Justice

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.

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，这是有效与人工智能进行互动和批判性评估所需的能力。最重要的是，必须让更广泛的青年了解这些技术如何运作，它们对个人和社会的影响，以及它们如何增加或破坏公平。迫切需要研究青年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交互。在这个项目中，高中年龄的年轻人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方式的方法，以解释其外部效果和影响。本提案是对亲爱的同事信（DCL）的回应：“在正式和非正式环境中快速加速K-12教育中的人工智能研究（NSF 23-097）”，并由学生和教师创新技术经验（ITEST）计划资助，该计划支持建立对实践，计划要素，有助于增加学生对科学，技术，工程，和数学（STEM）和信息和通信技术（ICT）的职业生涯。研究人员将开展一个-一项为期一年的研究，涉及高中青年为他们的同龄人设计日常机器学习应用程序，并合作进行算法审计，重点是算法的公平性，问责制和正义。 本研究将探讨以下研究问题：（1）高中生目前对日常机器学习应用的体验和理解是什么？(2)高中生如何设计和进行机器学习应用程序的协作审核？以及（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，并参加扩展研讨会，以设计和协作审核各种机器学习应用程序，包括文本，声音和图像。使用共同设计，访谈和观察方法的组合，研究人员将深入了解这些系统中最常见的用户的年轻人进行算法审计的可行性;生产性最终用户算法审计的协作互动动态;以及年轻人通过审计对算法正义的理解。以前的最终用户算法审计研究只关注非专家成年人。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。