The use of artificial intelligence in scientific research  with integrity and ethics

Ricardo Limongi

doi:10.37497/rev.artif.intell.educ.v5i00.22

Authors

Ricardo Limongi Federal University of Goiás (UFG), Goiás https://orcid.org/0000-0003-3231-7515

DOI:

https://doi.org/10.37497/rev.artif.intell.educ.v5i00.22

Keywords:

Artificial intelligence, Research ethics, Ethical governance, Scientific integrity, Algorithmic transparency

Abstract

This paper addresses the evolution of Artificial Intelligence (AI) in scientific research and the ethical and integrity challenges that arise with its integration. AI has become an indispensable tool for researchers, accelerating discoveries and optimizing processes. However, using these algorithms raises concerns about bias, transparency, and accountability. The ability of machines to learn and create knowledge challenges the paradigms of authorship and credibility, putting integrity and ethics under new scrutiny. The discussion emphasizes robust ethical governance, collaboration among stakeholders, ongoing education, and the creation of transparent and auditable algorithms. It further highlights the importance of maintaining ethics and integrity at the heart of AI research to ensure its advancement benefits humanity fairly and responsibly, emphasizing the need for a holistic approach involving education, transparency, accountability, and active participation of multiple stakeholders. Finally, it reiterates that as we embark on this new era of AI-driven discovery, we must embrace both the opportunities and the ethical challenges it presents, ensuring that the use of AI in scientific research continues to benefit humanity by promoting knowledge and well-being.

Author Biography

Ricardo Limongi, Federal University of Goiás (UFG), Goiás

Doctor in Business Administration in the field of Marketing Strategies from EAESP/FGV, with a doctoral internship at Cornell University under the supervision of Vithala. Post-doctorate in Behavioral Economics applied to Marketing at UnB and Post-Doctorate in Machine Learning applied to Marketing at UFRGS. Adjunct Professor III at the Federal University of Goiás (UFG). Permanent Professor, and coordinator in the biennium (2020-2022), of the Graduate Program in Administration. MBA Coordinator in Strategic Marketing. Visiting Professor and coordinated the inter-institutional collaboration project at the University of Santiago de Chile (USACH). He has additional training in Spatial Statistics, Data Science, and Machine Learning. His research has already been nominated and/or awarded by the international Emerald database (2015/2017) and scientific events. His topics of interest are: Applied Marketing Performance, company or consumer level; Marketing Analytics, and Machine Learning.

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