[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127040-en":3,"doc-seo-127040-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127040,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Youth as Peer Auditors - Engaging Teenagers with Algorithm Auditing of Machine Learning Applications","As AI/ML applications become increasingly embedded in young people’s daily lives, enabling youth to meaningfully interact, design, and evaluate these systems is essential. This paper presents a youth-as-peer-auditor approach to help teenagers examine the opaque inner workings of ML-powered applications and reason about external impacts. In a two-week workshop, 13 participants designed and audited each other’s applications, with pre/post clinical interviews showing improved bias identification, concerns about dataset and model design, and discussion of algorithmic justice and model improvements.","arXiv :2404 .05874v3 [ cs .HC] 16 Apr 2024  \nYouth as Peer Auditors: Engaging Teenagers with Algorithm Auditing of Machine Learning Applications  \nLUIS MORALES-NAVARRO, University of Pennsylvania, United States YASMIN B. KAFAI, University of Pennsylvania, United States  \nVEDYA KONDA, University of Pennsylvania, United States DANAË METAXA, University of Pennsylvania, United States  \nAs artificial intelligence/machine learning (AI/ML) applications become more pervasive in youth lives, supporting them to interact, design, and evaluate applications is crucial. This paper positions youth as auditors of their peers’ML-powered applications to better understand algorithmic systems’ opaque inner workings and external impacts. In a two-week workshop, 13 youth (ages 14-15) designed and audited ML-powered applications. We analyzed pre/post clinical interviews in which youth were presented with auditing tasks. The analyses show that after the workshop all youth identified algorithmic biases and inferred dataset and model design issues. Youth also discussed algorithmic justice issues and ML model improvements. Furthermore, youth reflected that auditing provided them new perspectives on model functionality and ideas to improve their own models. This work contributes (1) a conceptualization of algorithm auditing for youth; and (2) empirical evidence of the potential benefits of auditing. We discuss potential uses of algorithm auditing in learning and child-computer interaction research.  \nCCS Concepts: • Human-centered computing → Empirical studies in HCI; • Social and professional topics → K-12 education; Computing literacy.  \nAdditional Key Words and Phrases: youth, algorithm auditing, algorithmic justice, machine learning, child-computer interaction, artificial intelligence  \nACM Reference Format:  \nLuis Morales-Navarro, Yasmin B. Kafai, Vedya Konda, and Danaë Metaxa. 2024. Youth as Peer Auditors: Engaging Teenagers with Algorithm Auditing of Machine Learning Applications. In Interaction Design and Children (IDC ’24), June 17–20, 2024, Delft, Netherlands. ACM, New York, NY, USA, 22 pages. [https://doi.org/10.1145/3628516.3655752](https://doi.org/10.1145/3628516.3655752)  \n1 INTRODUCTION  \nToday, children and youth interact with artificial intelligence/machine learning (AI/ML)-powered applications and algorithmic systems when they socialize with friends, go to school, play games, listen to music, do homework, order food, or watch videos. Given the increasing prevalence of AI/ML in their lives, it is crucial to provide young people with the necessary support to engage with, create, and evaluate AI/ML applications. As such, child-computer interaction (CCI) research on AI/ML literacy has received increasing attention [28, 40, 62] . An obstacle in supporting young people in understanding and engaging with AI/ML ideas is the lack of transparency in ML models. Furthermore, existing research gives little attention to critical issues of computational empowerment [16] such as supporting youth in thinking  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, [requires prior specific permission and/or a fee. Request permissions from permissions@acm.org](requires prior specific permission and/or a fee. Request permissions from permissions@acm.org).  \n© 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM.  \nManuscript submitted to ACM  \nIDC ’24, June 17–20, 2024, Delft, Netherlands Morales-Navarro, Kafai, Konda, & Metaxa  \nabout the limitations and implications of AI/ML technologies [60] or in c","cbCaijVCYW3eGSTu","https://ap.wps.com/l/cbCaijVCYW3eGSTu","pdf",33283381,1,22,"English","en",105,"# Introduction\n## Youth and AI/ML in daily life\n## Algorithm auditing as a research method\n## Research questions and workshop approach","[{\"question\":\"What is the central idea of the paper?\",\"answer\":\"The paper positions youth as auditors of their peers’ ML-powered applications to better understand opaque algorithmic systems and their external impacts.\"},{\"question\":\"How was the study conducted?\",\"answer\":\"A two-week workshop involved 13 youth (ages 14–15) who designed and audited each other’s ML applications, followed by pre/post clinical interviews and post-workshop reflections.\"},{\"question\":\"What changes did the researchers observe from pre to post?\",\"answer\":\"After the workshop, all participants identified potential algorithmic biases and inferred issues related to dataset and model design, while also discussing algorithmic justice and ways to improve models.\"}]","Youth as Peer Auditors - 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