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When Artificial Intelligence Enters the Research Process: Implications for Data Integrity in Human Participants
1College of Doctoral Studies, Institutional Review Board, Grand Canyon University, Phoenix, AZ, USA.
Summary
Generative artificial intelligence (AI) introduces new risks to human participants research, including data fabrication and altered participant responses. New strategies and guidelines are needed to ensure research integrity and ethical oversight in AI-enabled environments.
Area of Science:
- Social Sciences
- Computer Science
- Research Ethics
Background:
- Generative artificial intelligence (AI) is rapidly integrating into academic and professional workflows.
- Previous focus has been on AI for writing and analysis, neglecting its impact on data generation and integrity.
- Emerging risks include data fabrication and AI-mediated participant responses in human research.
Purpose of the Study:
- To examine the emerging risks of generative AI in human participants research.
- To explore implications for research ethics oversight, including institutional review boards and ethics committees.
- To propose practical strategies for mitigating AI-related risks in research.
Main Methods:
- Invited commentary examining current literature and emerging trends.
- Analysis of risks related to data authenticity, measurement validity, and evidence basis.
- Exploration of ethical considerations for AI-enabled research environments.
Main Results:
- Generative AI challenges fundamental assumptions about data authenticity and validity.
- New risks necessitate re-evaluation of research ethics oversight and accountability.
- Transparency, data provenance, and methodological rigor are critical in AI research.
Conclusions:
- Human participants research requires a balance of trust and verification mechanisms.
- Development of clear institutional and international guidelines for responsible AI integration is essential.
- Proactive strategies are needed to mitigate risks and maintain research integrity.
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