# Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review

arXiv: https://arxiv.org/abs/2412.16369 · HTML: https://mlsanity.com/paper/2412.16369/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Identifying and Mitigating the Security Risks of Generative AI](https://mlsanity.com/paper/2308.14840/) (2023)
- [Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality](https://mlsanity.com/paper/2310.19287/) (2023)
- [Creative beyond TikToks: Investigating Adolescents' Social Privacy Management on TikTok](https://mlsanity.com/paper/2301.11600/) (2023)

## Built on by (most cited first, 5 of 5)

- [Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI](https://mlsanity.com/paper/2503.11950/) (2025)
- [Covert Surveillance in Smart Devices: A SCOUR Framework Analysis of Youth Privacy Implications](https://mlsanity.com/paper/2510.24072/) (2025)
- [Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives](https://mlsanity.com/paper/2501.13320/) (2025)
- [Balancing Usability and Compliance in AI Smart Devices: A Privacy-by-Design Audit of Google Home, Alexa, and Siri](https://mlsanity.com/paper/2601.04403/) (2026)
- [Toward Youth-Centered Privacy-by-Design in Smart Devices: A Systematic Review](https://mlsanity.com/paper/2601.11598/) (2026)

## Abstract (arXiv)

This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems, focusing on social media platforms, educational technology, gaming systems, and recommendation algorithms. Using a rigorous methodology, the review started with 2,000 papers, narrowed down to 552 after initial screening, and finally refined to 108 for detailed analysis. Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, transparency expectations, and strategies to enhance user control over personal data. Findings reveal significant privacy concerns among young users, including a perceived lack of control over personal information, potential misuse of data by AI, and fears of data breaches and unauthorized access. These issues are worsened by unclear data collection practices and insufficient transparency in AI applications. The intention to share data is closely associated with perceived benefits and data protection assurances. The study also highlights the role of parental mediation and the need for comprehensive education on data privacy. Balancing privacy and utility in AI applications is crucial, as young digital citizens value personalized services but remain wary of privacy risks. Trust in AI is significantly influenced by transparency, reliability, predictable behavior, and clear communication about data usage. Strategies to improve user control over personal data include access to and correction of data, clear consent mechanisms, and robust data protection assurances. The review identifies research gaps and suggests future directions, such as longitudinal studies, multicultural comparisons, and the development of ethical AI frameworks. The findings have significant implications for policy development and educational initiatives