Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance

• URL: http://arxiv.org/abs/2503.11947v1
• Date: Sat, 15 Mar 2025 01:35:56 GMT
• Title: Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance
• Authors: Austin Shouli, Ankur Barthwal, Molly Campbell, Ajay Kumar Shrestha,
• Abstract summary: The rapid expansion of Artificial Intelligence in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection.<n>While AI-driven personalization offers enhanced user experiences, it often operates without clear ethical boundaries, leaving young users vulnerable to data exploitation and algorithmic biases.<n>This paper presents a call to action for ethical AI governance, advocating for a structured framework that ensures youth-centred privacy protections, transparent data practices, and regulatory oversight.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid expansion of Artificial Intelligence (AI) in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection. While AI-driven personalization offers enhanced user experiences, it often operates without clear ethical boundaries, leaving young users vulnerable to data exploitation and algorithmic biases. This paper presents a call to action for ethical AI governance, advocating for a structured framework that ensures youth-centred privacy protections, transparent data practices, and regulatory oversight. We outline key areas requiring urgent intervention, including algorithmic transparency, privacy education, parental data-sharing ethics, and accountability measures. Through this approach, we seek to empower youth with greater control over their digital identities and propose actionable strategies for policymakers, AI developers, and educators to build a fairer and more accountable AI ecosystem.

Related papers

• Privacy Ethics Alignment in AI (PEA-AI): A Stakeholder-Centric Based Framework for Ethcial AI [0.0]
  This study explores evolving privacy concerns across three key stakeholder groups.<n>Young users emphasize autonomy and digital freedom.<n>Parents and educators advocate for regulatory oversight and AI literacy programs.<n> AI professionals prioritize the balance between ethical system design and technological efficiency.
  arXiv  Detail & Related papers  (2025-03-15T01:42:45Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents [75.85554113398626]
  We develop a benchmark called AgentDAM to evaluate how well existing and future AI agents can limit processing of potentially private information. Our benchmark simulates realistic web interaction scenarios and is adaptable to all existing web navigation agents.
  arXiv  Detail & Related papers  (2025-03-12T19:30:31Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs.<n>Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.<n>Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives [0.0]
  This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals.<n>Using qualitative analysis of survey responses from 227 participants, the research identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection.<n>The findings provide actionable insights into balancing the benefits of AI with robust privacy protections.
  arXiv  Detail & Related papers  (2025-01-23T02:06:25Z)
• Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review [0.0]
  This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems.<n>Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, and strategies to enhance user control over personal data.<n>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.
  arXiv  Detail & Related papers  (2024-12-20T22:00:06Z)
• Transparency, Security, and Workplace Training & Awareness in the Age of Generative AI [0.0]
  As AI technologies advance, ethical considerations, transparency, data privacy, and their impact on human labor intersect with the drive for innovation and efficiency.<n>Our research explores publicly accessible large language models (LLMs) that often operate on the periphery, away from mainstream scrutiny.<n>Specifically, we examine Gab AI, a platform that centers around unrestricted communication and privacy, allowing users to interact freely without censorship.
  arXiv  Detail & Related papers  (2024-12-19T17:40:58Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Ethics and Responsible AI Deployment [1.3597551064547502]
  Article explores the need for ethical AI systems that safeguard individual privacy while complying with ethical standards. Research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and ethical guidelines.
  arXiv  Detail & Related papers  (2023-11-12T13:32:46Z)
• Users are the North Star for AI Transparency [111.5679109784322]
  Despite widespread calls for transparent artificial intelligence systems, the term is too overburdened with disparate meanings to express precise policy aims or to orient concrete lines of research. Part of why this happens is that a clear ideal of AI transparency goes unsaid in this body of work. We explicitly name such a north star -- transparency that is user-centered, user-appropriate, and honest.
  arXiv  Detail & Related papers  (2023-03-09T18:53:29Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.