Investigating Youth AI Auditing

2025-02-25

AI Summary

This study addresses the longstanding exclusion of adolescents from responsible AI (RAI) practice by systematically investigating their potential and pathways for participation in AI auditing. Through participatory workshops with 17 adolescents under 18, we employed customized AI auditing tools and scaffolded instruction grounded in critical AI literacy to support their auditing of youth-frequent AI systems—including text generation and search auto-completion. Results provide the first empirical evidence that adolescents can independently identify diverse algorithmic biases and risky behaviors by drawing on lived experience, sociocultural identity, and domain-specific interests; audit quality is demonstrably experience-driven. The study introduces the novel “intergenerational co-auditing” framework, offering a replicable methodology and empirical foundation for inclusive AI governance, STEM education reform, and adolescent digital empowerment.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Audits of web technologies, standards, platforms, and applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Youth are active users and stakeholders of artificial intelligence (AI), yet they are often not included in responsible AI (RAI) practices. Emerging efforts in RAI largely focus on adult populations, missing an opportunity to get unique perspectives of youth. This study explores the potential of youth (teens under the age of 18) to engage meaningfully in RAI, specifically through AI auditing. In a workshop study with 17 teens, we investigated how youth can actively identify problematic behaviors in youth-relevant ubiquitous AI (text-to-image generative AI, autocompletion in search bar, image search) and the impacts of supporting AI auditing with critical AI literacy scaffolding with guided discussion about AI ethics and an auditing tool. We found that youth can contribute quality insights, shaped by their expertise (e.g., hobbies and passions), lived experiences (e.g., social identities), and age-related knowledge (e.g., understanding of fast-moving trends). We discuss how empowering youth in AI auditing can result in more responsible AI, support their learning through doing, and lead to implications for including youth in various participatory RAI processes.

Problem

• Youth inclusion in AI auditing
• Identifying AI issues via youth perspectives
• Enhancing responsible AI with youth insights

Innovation

• Youth AI auditing engagement
• Critical AI literacy scaffolding
• Ethical AI guided discussion