Explainable artificial intelligence and algorithmic transparency: challenges and advances in automated decision-making

Authors

Keywords:

Explainable artificial intelligence, algorithmic transparency, automated decisions, digital ethics, technological governance

Abstract

The growing reliance on automated systems in fields such as healthcare, justice, finance, and public administration has intensified the need to understand how artificial intelligence (AI) models influence high-impact decisions. Explainable AI (XAI) responds to algorithmic opacity by promoting mechanisms that enable the interpretation, auditing, and justification of intelligent systems’ predictions or recommendations. Beyond technical performance, explainability becomes an essential ethical and legal principle to foster public trust, mitigate discriminatory biases, and protect fundamental rights. In this context, notable progress has been made in interpretability techniques, ranging from intrinsically transparent models to post-hoc tools that visualize internal patterns. Nevertheless, significant challenges persist, including the lack of standardized metrics, the trade-off between performance and explainability, and the pressing need for robust regulatory frameworks defining responsibilities and usage boundaries. The consolidation of XAI represents a key inflection point for algorithmic governance, enabling safer, fairer, and more understandable interactions between humans and automated systems.

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References

Alayón Miranda, S. (2024). El problema de la interpretabilidad de la inteligencia artificial y su impacto en la administración pública. Revista Canaria de Administración Pública, 3, 175–202. https://revistacanarias.tirant.com/index.php/revista-canaria/article/view/42

Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica, Online Edition.

Corvalán, J. G. (2018). Inteligencia artificial: retos, desafíos y oportunidades – Prometea: la primera inteligencia artificial de Latinoamérica al servicio de la Justicia. Revista de Investigações Constitucionais, 5(1), 295-316. https://doi.org/10.5380/rinc.v5i1.55334

European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonized rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52021PC0206

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

García-Vera, Y. S., & Juca-Maldonado, F. X. (2026). Inteligencia Artificial Aplicada a la Contabilidad: Flujos, buenas prácticas y control. Sophia Editions.

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1-42. https://doi.org/10.1145/3236009

Hernández Sampieri, R., Fernández, C., & Baptista, P. (2014). Metodología de la investigación. 6ta. Ed. Mc. Graw Hill Eduaction, México.

Hulsen, T. (2023). Explainable Artificial Intelligence (XAI): Concepts and challenges in healthcare. AI (Basel, Switzerland), 4(3), 652–666. https://doi.org/10.3390/ai4030034

Ibarra-Pincay, M., & Alcívar-Cevallos, R. (2024). Tendencias de la Inteligencia Artificial Explicable en el Área de Psicología. Revista Científica INGENIAR: Ingeniería, Tecnología E Investigación, 7(13), 80-101. https://journalingeniar.org/index.php/ingeniar/article/view/162

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, USA.

McGrath, A., & Jonker, A. (2024). What is AI interpretability? IBM Knowledge Center. https://www.ibm.com/es-es/think/topics/interpretability

Molnar, C. (2020). Interpretable Machine Learning. Lulu.com.

Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura. (2024). Ética de la inteligencia artificial. UNESCO. https://www.unesco.org/es/artificial-intelligence/recommendation-ethics

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, United States.

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Published

2026-04-01

Issue

Vol. 4 No. 2 (2026): Innovación científica, transformación digital y desafíos jurídicos en el contexto contemporáneo (abril-junio)

Section

Artículos

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Copyright (c) 2026 Génesis Karolina Robles-Zambrano, Ingrid Joselyne Diaz-Basurto, Deinier Ros-Álvarez, Verónica Zuleyka Ramírez-Bósquez

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