Implications of artificial intelligence in research methodology
DOI:
https://doi.org/10.36825/RITI.12.26.003Keywords:
Artificial Intelligence, Research Methodology, Ethics, Algorithmic Biases, Data ProtectionAbstract
This systematic review explores how advanced data analysis technologies are transforming scientific research methodology. Sources describing the transformation of various stages of the research process by these tools, from hypothesis generation to results interpretation, were examined. These technologies offer unprecedented possibilities to enhance research. They allow for the identification of complex patterns, such as subtle trends in genomic data or unexpected correlations in sociological studies, which might go unnoticed with traditional methods. Additionally, they automate repetitive tasks, freeing up time for more in-depth analysis. Significant ethical and practical challenges are highlighted. Algorithmic biases, for example, could perpetuate existing prejudices in training data, affecting the validity of results in fields such as medical or socioeconomic research. Concerns also arise regarding the privacy of study participants and the risk of exclusion for groups underrepresented in the data. The review concludes that adopting a holistic and responsible approach is imperative. In practice, this involves developing robust ethical frameworks, fostering collaboration between disciplines such as computer science, ethics, and social sciences, investing in specialized education, promoting transparency in algorithmic processes, and strengthening global cooperation through responsible data sharing and the development of common research standards.
References
Cetina, K. (1999). La fabricación del conocimiento: Un ensayo sobre el carácter constructivista y contextual de la ciencia. Prensa de la Universidad de Pergamon.
Dunjko, V., Briegel, H. J. (2018). Aprendizaje automático de propósitos: fuerza de entrenamiento de las ciencias. Nature Reviews Physics, 1 (2), 109-122.
Kitchin, R. (2014). La revolución de los datos: grandes datos, código abierto y algoritmos de redes. SAGE Publications.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., Floridi, L. (2016). La ética de los algoritmos: Cartografiando la cuestión ética. Big Data & Society, 3 (2), 1-21. https://doi.org/10.1177/2053951716679679
Fazelpour, S., Liaskos, S. (2020). Interpretabilidad y explicación en sistemas de IA para el aprendizaje automático: Un estudio de la literatura. 52ª Conferencia Anual de la IEEE sobre Sistemas Inteligentes, 9827-9831.
Bostrom, N. (2014). Superinteligencia: Caminos, peligros, estrategias. Oxford University Press.
Zeng, Y. (2018). Ética IA: impacto en investigación y diseño de política inteligentes. Inteligencia Artificial, 262, 92-98.
Ruiz Muñoz, G. (2024). Exploración del impacto del metaverso y la inteligencia artificial en la educación superior. Identidad Bolivariana, 8 (2), 32-45. https://doi.org/10.37611/IB8ol232-45
King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L. N., Sparkes, A., Whelan, K., Clare, A. (2009). The automation of science. Science, 324 (5923), 85-89. https://doi.org/10.1126/science.1165620
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542 (7639), 115-118. https://doi.org/10.1038/nature21056
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316 (22), 2402-2410. https://doi.org/10.1001/jama.2016.17216
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P. M., Zietz, M., Hoffman, M. M., Xie, W., Rosen, G. L., Lengerich, B. J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A. E., Shrikumar, A., Xu, J., Cofer, E. M., Lavander, C. A., Turaga, S. C., Alexandari, A. M., Lu, Z., Harris, D. J., DeCaprio, D., Qi, Y., Kundaje, A., Peng, Y., Wiley, L. K., Segler, M., Boca, S., M., Swamidass, J., Huang, A., Gitter, A., Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15 (141), 20170387. https://doi.org/10.1098/rsif.2017.0387
Athey, S. (2018). The impact of machine learning on economics. En A. Agrawal, J. Gans, A. Goldfarb (Eds.), The Economics of Artificial Intelligence: An Agenda (pp. 507-547). University of Chicago Press.
Alipanahi, B., Delong, A., Weirauch, M. T., Frey, B. J. (2015). Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature Biotechnology, 33 (8), 831-838. https://doi.org/10.1038/nbt.3300
Shaheed, N., Eldridge, K., Wang, T., Wagner, S., Olsen, A., Gao, Y. (2019). SciBERT: Pretrained contextualized embeddings for scientific text. arXiv preprint. https://doi.org/10.48550/arXiv.1903.10676.
Westergaard, D., Stærfeldt, H. H., Tønsberg, C., Jensen, L. J., Brunak, S. (2018). A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts. PLoS Computational Biology, 14 (2), 1-16. https://doi.org/10.1371/journal.pcbi.1005962
Dhamdhere, K., McCurley, K., Nahmias, R., Sundararajan, M., Yan, Q. (2017). Analyza: Exploring data with conversation. Proceedings of the 22nd International Conference on Intelligent User Interfaces, Limassol, Cyprus.
Bender, E. M., Gebru, T., McMillan-Major, A., Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event Canada. https://doi.org/10.1145/3442188.3445922
Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., Telenti, A. (2019). A primer on deep learning in genomics. Nature genetics, 51 (1), 12-18. https://doi.org/10.1038/s41588-018-0295-5
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28 (4), 689-707. https://doi.org/10.1007/s11023-018-9482-5
Gómez-González, E., Gómez, E. (2020). Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact. Publications Office of the European Union. https://doi.org/10.2760/047666
Long, D., Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, USA. https://doi.org/10.1145/3313831.3376727
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1 (5), 206-215. https://doi.org/10.1038/s42256-019-0048-x
Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer International Publishing. https://doi.org/10.1007/978-3-030-30371-6
West, S. M., Whittaker, M., Crawford, K. (2019). Discriminating Systems: Gender, Race and Power in AI. AI Now Institute. https://ainowinstitute.org/publication/discriminating-systems-gender-race-and-power-in-ai-2
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., Floridi, L. (2018). Artificial Intelligence and the 'Good Society': the US, EU, and UK approach. Science and Engineering Ethics, 24 (2), 505-528. https://doi.org/10.1007/s11948-017-9901-7
Comisión Europea. (2019). Ethics guidelines for trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
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