# Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives

arXiv: https://arxiv.org/abs/2501.13320 · HTML: https://mlsanity.com/paper/2501.13320/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## 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)
- [Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior](https://mlsanity.com/paper/2307.10492/) (2023)
- [Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review](https://mlsanity.com/paper/2412.16369/) (2024)
- [Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality](https://mlsanity.com/paper/2310.19287/) (2023)

## Built on by (most cited first, 2 of 2)

- [Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI](https://mlsanity.com/paper/2503.11950/) (2025)
- [Toward Youth-Centered Privacy-by-Design in Smart Devices: A Systematic Review](https://mlsanity.com/paper/2601.11598/) (2026)

## Abstract (arXiv)

As Artificial Intelligence (AI) systems become increasingly integrated into various aspects of daily life, concerns about privacy and ethical accountability are gaining prominence. This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals. Using qualitative analysis of survey responses from 227 participants, the research identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection, alongside perceived benefits such as personalized services, enhanced efficiency, and educational advancements. Stakeholders emphasized the need for transparency, privacy-by-design, user empowerment, and ethical oversight to address privacy concerns effectively. The findings provide actionable insights into balancing the benefits of AI with robust privacy protections, catering to the diverse needs of stakeholders. Recommendations include implementing selective data use, fostering transparency, promoting user autonomy, and integrating ethical principles into AI development. This study contributes to the ongoing discourse on ethical AI, offering guidance for designing privacy-centric systems that align with societal values and build trust among users. By addressing privacy challenges, this research underscores the importance of developing AI technologies that are not only innovative but also ethically sound and responsive to the concerns of all stakeholders.