Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI

Created by
  • Haebom

Author

Ankur Barthwal, Molly Campbell, Ajay Kumar Shrestha

Outline

This study explores how privacy dynamics have changed, particularly for young digital citizens navigating data-centric environments, as artificial intelligence (AI) has become integrated into digital ecosystems. We examine the evolving privacy concerns of three key stakeholder groups—digital citizens aged 16 to 19, parents/educators, and AI experts—and assess differences in data ownership, trust, transparency, parental mediation, education, and risk-benefit perceptions. Using grounded theory methodology, we synthesize insights from 482 participants through structured surveys, qualitative interviews, and focus groups. Our findings reveal that young users emphasize autonomy and digital freedom, while parents and educators advocate for regulatory oversight and AI literacy programs. AI experts prioritize a balance between ethical system design and technical efficiency. We also highlight gaps in AI literacy and transparency, and emphasize the need for a comprehensive stakeholder-centered privacy framework that accommodates the needs of diverse users. We use comparative thematic analysis to identify key tensions in privacy governance and develop a novel AI Privacy-Ethics Alignment (PEA-AI) model that structures privacy decision-making as a dynamic negotiation among stakeholders. By systematically analyzing topics such as transparency, user control, risk awareness, and parental moderation, we provide a scalable and adaptive foundation for AI governance, ensuring that privacy protections evolve alongside new AI technologies and youth-centered digital interactions.

Takeaways, Limitations

Takeaways:
By revealing differing privacy expectations among young digital citizens, parents/educators, and AI experts, we highlight the need to develop a comprehensive privacy framework that meets the needs of diverse stakeholders.
Emphasizes the importance of AI literacy and transparency, and suggests the need for AI literacy programs and regulatory oversight.
The PEA-AI model provides a scalable and adaptable foundation for AI governance by proposing a privacy decision-making structure through dynamic negotiation between stakeholders.
Limitations:
Limitations on generalization as the study subjects were limited to digital citizens of a specific age group (16-19 years old).
Difficulty in generalizing research results due to them being limited to specific regions or cultural backgrounds.
Further research is needed on the practical application and effectiveness of the PEA-AI model.
👍