CHALLENGES AND ETHICAL ASPECTS OF USING ARTIFICIAL INTELLIGENCE IN BIOLOGICAL RESEARCH (REVIEW)
DOI:
https://doi.org/10.62405/osiconf.2026.31Keywords:
artificial intelligence, machine learning, neuronic networks, large language models, biology, ecology, research ethics, academic integrity / research misconduct.Abstract
The purpose of this paper is to review the major challenges related to applying AI in biological research and to outline the main aspects of its ethical use. The research methods are based on analyzing published papers and our own experience. The review of recent scientific papers is devoted to revealing the positive experience and negative consequences of using AI instruments in biological and ecological research. Examples of successful use of AI in molecular biology, medical science, ecology, biodiversity studies, hydrobiology and other branches of biology are provided. The paper outlines the issues and drawbacks of using AI in research and the existing methodological approaches to its ethical use.
References
Cowans, A., Lambin, X., Hare, D., & Sutherland, C. (2026). Improving the integration of artificial intelligence into existing ecological inference workflows. Methods in Ecology and Evolution, 17, 228–237. https://doi.org/10.1111/2041-210X.14485
Guetterman, T.C., James, T.G., & DeJonckheere M. (2026). Ethical considerations for the use of artificial intelligence in qualitative research: Questions for researchers, ethic boards, and reviewers. Methods in Psychology, 15, 100266. https://doi.org/10.1016/j.metip.2026.100266
Hein, Z.M., Guruparan, D., Okunsai, B., Che Mohd Nassir, C.M.N., Ramli, M.D.C., & Kumar, S. (2025). AI and Machine Learning in Biology: From Genes to Proteins. Biology, 14, 1453. https://doi.org/10.3390/biology14101453
Khamitov, N.V. (2026). Human — artificial intelligence interaction: a methodology for understanding challenges and prospects. Visn. Nac. Akad. Nauk Ukr, 7, 13–24. https://doi.org/10.15407/visn2026.07.013 [in Ukrainian].
Kitzes, J., Chronister, L., Czarnecki, C., Fiss, C., Freeland-Haynes, L., Goodman, B. D., Lapp, S., Lyon, R. P., Nossan, H., Rhinehart, T. A., Ruiz Guzman, S., Syunkova, A., & Viotti, L. (2026). Integrating AI models into ecological research workflows: The case of terrestrial bioacoustics. Methods in Ecology and Evolution, 17, 257–271. https://doi.org/10.1111/2041-210x.70133
Lawrence, E., El-Shazly, A., Seal, S., Joshi, C. K., Liò, P., Singh, S., Bender, A., Spjuth, O., Sormanni, P., & Greenig, M. (2024). Understanding biology in the age of artificial intelligence [Preprint]. arXiv. https: doi.org10.48550/arXiv.2403.04106
‘Lhoumeau, S., Pinelo, J., & Borges, P.A.V. (2025). Artificial intelligence for biodiversity: Exploring the potential of recurrent neural networks in forecasting arthropod dynamics based on time series. Ecological Indicators, 171, 113119, https://doi.org/10.1016/j.ecolind.2025.113119
Living guidelines on the responsible use of generative AI in research (2026). ERA Forum Stakeholders’ document. Third version, May 2026. European Commission Directorate-General for Research and Innovation. https://surl.li/xbffud
Miao, Z., Zhang, Y., Fabian, Z., Hernandez Celis, A., Beery, S., Li, C., Liu, Z., Gupta, A., Nasir, M., Li, W., Holmberg, J., Palmer, M., Gaynor, K., Arbelaez, P., Wang, P., Dodhia, R., & Ferres, J. L. (2026). New frontiers in artificial intelligence for biodiversity research and conservation with multimodal language models. Methods in Ecology and Evolution, 17, 238–256. https://doi.org/10.1111/2041-210x.70120
Miller, T., Micho´nski, G., Durlik, I., Kozlovska, P., & Biczak, P. (2025). Artificial intelligence in aquatic biodiversity research: A PRISMA-Based Systematic Review. Biology, 14, 520. https://doi.org/10.3390/biology14050520
Mishra, G. (2026). Ecology is not yet ready for AI – and why that matters. Proc. Natl. Acad. Sci. U.S.A., 123(22), e2533976123. https://doi.org/10.1073/pnas.2533976123
Qi, C., Wang, W., Jiang, S., Liu, Q., Song, X., Fang, H., & Wei, Z. (2026). Artificial Intelligence agents for biological research: a survey. Briefings in Bioinformatics, 27(1), bbag075. https://doi.org/10.1093/bib/bbag075
Resnik, D.B., & Hosseini, M. (2025). The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool. AI Ethics, 5, 1499–1521. https://doi.org/10.1007/s43681-024-00493-8
Scientific and technological leadership is the main factor of the strength of the state and society (XXXVI Academician V.I. Vernadsky Readings) (2026). Visn. Nac. Akad. Nauk Ukr., 4, 80–88. https://nasu-periodicals.org.ua/index.php/visnyk/article/view/28837 [in Ukrainian].
Тhe Law of Ukraine “On Academic Integrity” (2026). Bulletin of the Verkhovna Rada of Ukraine, 13, 35. https://zakon.rada.gov.ua/laws/show/4742-20#Text [in Ukrainian].
Urban, J., Císar, P., & Pautsina, A. (2013). Artificial Intelligence in Biology. Technical Computing Prague conference. https://surl.li/tqdqam
Wilson, N. (2024). Artificial intelligence helps drive new frontiers in ecology. BioScience, 74,(5), 306–311. https://doi.org/10.1093/biosci/biae016
Wu, C.-I, & Li, C. (2024). Artificial intelligence and biological research. National Science Review, 11,(11), nwae415. https://doi.org/10.1093/nsr/nwae415
Yang, H., Feng, Q., Zouyi, L., Du, X., Xu, J., Xu, W., Liu, L., Wu, Z., & Zhang, Y. (2025). Artificial Intelligence in Aquatic Biology: Identifying and Conserving Aquatic Species. Water & Ecology, 1(2), 100008. https://doi.org/10.1016/j.wateco.2025.100008
Yunas, K., Singh, A., Copeland, M.M., Tytarenko, A.M., Kundrotas, P.J., Halfmann, R., Kasyanov, P.O., Feinberg, E.A., & Vakser, I.A. (2026). Simulation of cell-size systems at long timescales with flexible protein structures. bioRxiv. https://doi.org/10.64898/2026.06.20.733545
