Keywords
Research ethics
Ethical governance
Scientific integrity
Algorithmic transparency
How to Cite
Abstract
Purpose: This paper examines the evolution of Artificial Intelligence (AI) in scientific research, focusing on the ethical and integrity challenges arising from its integration into knowledge production.
Design/methodology/approach: A conceptual and exploratory analysis was conducted, drawing on interdisciplinary literature on AI governance, research ethics, and academic integrity practices. The study integrates insights from international policies, good practice guidelines, and current debates on algorithmic transparency and accountability.
Findings: The analysis reveals that AI has become an indispensable tool for accelerating scientific discovery and optimizing research processes. However, its use raises concerns related to bias, transparency, accountability, and authorship. The findings highlight the urgent need for robust ethical governance frameworks, transparent and auditable algorithms, and stronger collaboration among researchers, institutions, and policymakers.
Originality/value: This paper advances the debate on AI in science by integrating risks and opportunities in a single framework. It emphasizes that ethics and integrity must remain at the core of scientific progress and proposes a holistic approach that combines governance, education, responsibility, and multi-stakeholder participation.
Practical implications: The study underscores that the benefits of AI in research will only be realized if technological innovation is balanced with ethical values, ensuring that AI-driven discoveries contribute fairly and responsibly to human well-being.
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