Scientific Integrity in the Age of Artificial Intelligence: A Core Foundation for Innovative Research

Abstract

The increasing integration of artificial intelligence (AI) in scientific research raises crucial questions of integrity and ethics. Although AI offers advanced capabilities for analyzing, modeling, and exploring data, its use must adhere to strict standards of rigor and transparency. AI can simplify bias detection, experiment replication, and identification of fraudulent results. However, it also entails risks such as the excessive use of opaque algorithms, generalizing results from incomplete data, and unintentional manipulation of conclusions. Therefore, it is essential to establish precise guidelines for responsibly integrating AI into research methods, including scientist training, implementing model validation and auditing procedures, as well as revising standards for data communication and sharing. AI can also enhance scientific integrity by automating tedious tasks, facilitating interdisciplinary collaboration, and improving study reproducibility. Successful collaboration between AI and critical thinking is possible, enabling faster and more reliable scientific advancements. What are the best practices for using AI without compromising scientific integrity? How can researchers be trained and equipped for responsible AI use? What ethical and regulatory frameworks are necessary to oversee AI usage? This research field, at the intersection of ethical, methodological, and epistemological considerations, aims to advance science in a faster, more reliable, and responsible manner.

Keywords: Artificial intelligence (AI), Scientific research, Interdisciplinary collaboration, Methodological standards.

JEL Classification: 120

Paper type: Theoretical Research