Privacy Ethics Alignment in AI (PEA-AI): A Stakeholder-Centric Based Framework for Ethcial AI

• URL: http://arxiv.org/abs/2503.11950v1
• Date: Sat, 15 Mar 2025 01:42:45 GMT
• Title: Privacy Ethics Alignment in AI (PEA-AI): A Stakeholder-Centric Based Framework for Ethcial AI
• Authors: Ankur Barthwal, Molly Campbell, Ajay Kumar Shrestha,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing integration of Artificial Intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups, digital citizens (ages 16-19), parents, educators, and AI professionals, and assesses differences in data ownership, trust, transparency, parental mediation, education, and risk-benefit perceptions. Employing a grounded theory methodology, this research synthesizes insights from 482 participants through structured surveys, qualitative interviews, and focus groups. The findings reveal distinct privacy expectations- Young users emphasize autonomy and digital freedom, while parents and educators advocate for regulatory oversight and AI literacy programs. AI professionals, in contrast, prioritize the balance between ethical system design and technological efficiency. The data further highlights gaps in AI literacy and transparency, emphasizing the need for comprehensive, stakeholder-driven privacy frameworks that accommodate diverse user needs. Using comparative thematic analysis, this study identifies key tensions in privacy governance and develops the novel Privacy-Ethics Alignment in AI (PEA-AI) model, which structures privacy decision-making as a dynamic negotiation between stakeholders. By systematically analyzing themes such as transparency, user control, risk perception, and parental mediation, this research provides a scalable, adaptive foundation for AI governance, ensuring that privacy protections evolve alongside emerging AI technologies and youth-centric digital interactions.

Related papers

• Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance [0.0]
  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.
  arXiv  Detail & Related papers  (2025-03-15T01:35:56Z)
• 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.<n>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)
• Human Decision-making is Susceptible to AI-driven Manipulation [87.24007555151452]
  AI systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. This study examined human susceptibility to such manipulation in financial and emotional decision-making contexts.
  arXiv  Detail & Related papers  (2025-02-11T15:56:22Z)
• 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)
• Investigation of the Privacy Concerns in AI Systems for Young Digital Citizens: A Comparative Stakeholder Analysis [0.0]
  The study underscores the need for user-centric privacy controls, tailored transparency strategies, and targeted educational initiatives.<n> incorporating diverse stakeholder perspectives offers actionable insights into ethical AI design and governance.
  arXiv  Detail & Related papers  (2025-01-23T02:07:45Z)
• 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)
• 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)
• 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)
• More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence [62.3133247463974]
  We show that differential privacy can do more than just privacy preservation in AI. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI.
  arXiv  Detail & Related papers  (2020-08-05T03:07:36Z)

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.