Authors: Keenan Jones, Fatima Zahrah, Jason R.C. Nurse
Published on: April 17, 2024
Impact Score: 8.0
Arxiv code: Arxiv:2404.11515
Summary
- What is new: This article introduces key considerations for preserving participant privacy specifically in the burgeoning fields of computational social science, artificial intelligence, and data science research.
- Why this is important: The increasing reliance on advanced computational models for research in CSS, AI, and data science raises significant privacy concerns, especially with the potential for misuse leading to harm for individuals and society.
- What the research proposes: The paper proposes a set of considerations for researchers to integrate privacy protection into their research design, data collection, analysis, and dissemination phases to mitigate privacy risks.
- Results: By embedding privacy considerations early in the research process, the potential for privacy infringements can be significantly reduced, safeguarding individuals, especially vulnerable groups, from potential harms.
Technical Details
Technological frameworks used: nan
Models used: nan
Data used: nan
Potential Impact
This research could impact markets relying on data science and AI, including tech companies developing large language models, and could benefit privacy-focused tech companies, consultancy firms specializing in data protection, and regulatory bodies.
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