Inteligencia artificial, opacidad algorítmica y decisiones judiciales necesidad de establecer una moratoria
El estudio examina la viabilidad del uso de sistemas de inteligencia artificial (IA) en decisiones jurisdiccionales a partir de la problemática estructural de la opacidad algorítmica (OA). El trabajo contrasta las posturas que admiten niveles de opacidad funcionalmente tolerables con aquellas que sostienen que toda ininteligibilidad afecta la legitimidad democrática, la justicia epistémica y los derechos fundamentales. Asimismo, integra aportes de la XAI y del análisis probatorio para mostrar que las explicaciones disponibles no permiten reconstruir razonamientos, hechos ni vínculos causales indispensables en el proceso judicial. A través de una revisión exhaustiva de literatura reciente y del análisis de estándares normativos, filosóficos y procesales, se demuestra que las limitaciones técnicas, epistémicas, sociotécnicas e institucionales de los modelos actuales impiden garantizar condiciones mínimas de explicabilidad, auditabilidad y control racional. A la luz de estos hallazgos y del estándar interamericano sobre el derecho a comprender las razones de una decisión, se propone una moratoria precautoria que suspenda la incorporación de sistemas de IA en el ámbito jurisdiccional hasta que se validen estándares robustos de explicabilidad mediante procesos deliberativos interinstitucionales.
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Ahern, D. (2025). The New Anticipatory Governance Culture for Innovation: Regulatory Foresight, Regulatory Experimentation and Regulatory Learning. European Business Organization Law Review, (26), 241–283. https://doi.org/10.1007/s40804-025-00348-7
Alvarado, R. (2023). AI as an Epistemic Technology. Science and Engineering Ethics, 29(5), 32. https://doi.org/10.1007/s11948-023-00451-3
Bjerring, J. C., Mainz, J., & Munch, L. (2025). Deep learning models and the limits of explainable artificial intelligence. Asian Journal of Philosophy, 4(1), 1–26. https://doi.org/10.1007/s44204-024-00238-8
Boge, F. J. (2022). Two Dimensions of Opacity and the Deep Learning Predicament. Minds and Machines, 32(1), 43–75. https://doi.org/10.1007/s11023-021-09569-4
Cantarini, P. (2024). Algocracy, algorithmic institutionalism, digital rationality and risk to democracy. Revista Juridica Unicuritiba, 4(80), 590–626. https://doi.org/10.26668/revistajur.2316-753X.v4i80.7823
Carabantes, M. (2020). Black-box artificial intelligence: an epistemological and critical analysis. AI & Society, 35(2), 309–317. https://doi.org/10.1007/s00146-019-00888-w
Corte Interamericana de Derechos Humanos. (2016). Caso Maldonado Ordoñez vs. Guatemala. https://www.corteidh.or.cr/docs/casos/articulos/seriec_311_esp.pdf
Drnas de Clément, Z. (2008). Elementos Esenciales del Principio de Precaución Ambiental. In Universidad de Córdoba. Facultad de Derecho y Ciencias Sociales. Centro de Investigaciones Jurídicas y Sociales, Anuario X 2007 (1st ed., pp. 329 - 340). Buenos Aires: Universidad de Córdoba. Facultad de Derecho y Ciencias Sociales. Centro de Investigaciones Jurídicas y Sociales.
Engstrom, D. F. (2024). The Automated State: A Realist View. George Washington Law Review, 92(6), 1437–1472.
Facchini, A., & Termine, A. (2022). Towards a Taxonomy for the Opacity of AI Systems. In Studies in Applied Philosophy Epistemology and Rational Ethics (Vol. 63, pp. 73–89). https://doi.org/10.1007/978-3-031-09153-7_7
Fleisher, W. (2022). Understanding, Idealization, and Explainable AI. Episteme, 19(4), 534–560. https://doi.org/10.1017/epi.2022.39
Fraser, H., Simcock, R., & Snoswell, A. J. (2022). AI Opacity and Explainability in Tort Litigation. 2022 ACM Conference on Fairness Accountability and Transparency, 185–196. https://doi.org/10.1145/3531146.3533084
Freiman, O., McAndrews, J., Mansell, J., & van der Linden, C. (2025). ‘Opacity’ and ‘Trust’: From Concepts and Measurements to Public Policy. Philosophy & Technology, 38(1), 29. https://doi.org/10.1007/s13347-025-00862-z
Gerdes, A. (2021). Dialogical Guidelines Aided by Knowledge Acquisition: Enhancing the Design of Explainable Interfaces and Algorithmic Accuracy. In Advances in Intelligent Systems and Computing (Vol. 1288, pp. 243–257). https://doi.org/10.1007/978-3-030-63128-4_19
Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 205395171989794. https://doi.org/10.1177/2053951719897945
Grant, D. G., Behrends, J., & Basl, J. (2025). What we owe to decision-subjects: beyond transparency and explanation in automated decision-making. Philosophical Studies, 182(1), 55–85. https://doi.org/10.1007/s11098-023-02013-6
Hatherley, J. (2025). A moving target in AI-assisted decision-making: dataset shift, model updating, and the problem of update opacity. Ethics and Information Technology, 27(2), 20. https://doi.org/10.1007/s10676-025-09829-2
Kaas, M. H. L. (2024). The perfect technological storm: artificial intelligence and moral complacency. Ethics and Information Technology, 26(3), 49. https://doi.org/10.1007/s10676-024-09788-0
Knoks, A., & Raleigh, T. (2022). XAI and philosophical work on explanation: A roadmap. Ceur Workshop Proceedings, 3319, 101–106. https://ceur-ws.org/Vol-3319/paper11.pdf
Kroll, J. A. (2018). The fallacy of inscrutability. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180084. https://doi.org/10.1098/rsta.2018.0084
Kumar, M., Aijaz, A., Chattar, O., Shukla, J., & Mutharaju, R. (2024). Opacity, Transparency, and the Ethics of Affective Computing. IEEE Transactions on Affective Computing, 15(1), 4–17. https://doi.org/10.1109/TAFFC.2023.3278230
Langer, M., & König, C. J. (2023). Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management. Human Resource Management Review, 33(1), 100881. https://doi.org/10.1016/j.hrmr.2021.100881
Lasbleiz N. & Milkes I., (2021) Los desafíos de la automatización de las decisiones individuales por la Administración Pública, en “Disrupción tecnológica, transformación digital y sociedad, Ed. Externado.
Lee, F. (2025). The practices and politics of machine learning: a field guide for analyzing artificial intelligence. AI & SOCIETY. https://doi.org/10.1007/s00146-025-02430-7
Lo, F. T. H. (2024). The paradoxical transparency of opaque machine learning. AI and Society, 39(3), 1397–1409. https://doi.org/10.1007/s00146-022-01616-7
Lu, S. (2022). Data Privacy, Human Rights, and Algorithmic Opacity. California Law Review, 110(6), 2087–2147. https://doi.org/10.15779/Z38804XM07
Mann, S., Crook, B., Kastner, L., Schomacker, A., & Speith, T. (2023). Sources of Opacity in Computer Systems: Towards a Comprehensive Taxonomy. Proceedings 31st IEEE International Requirements Engineering Conference Workshops Rew 2023, 337–342. https://doi.org/10.1109/REW57809.2023.00063
Petrolo, M., Kubyshkina, E., & Primiero, G. (2023). A logical approach to algorithmic opacity. Ceur Workshop Proceedings, 3615, 89–95. https://ceur-ws.org/Vol-3615/short4.pdf
Riechmann, J & Tickner, J (2002). El principio de precaución. Barcelona: Icaria Editorial (pp. 47). Citado por González Villa, J (2006). Derecho Ambiental Colombiano: Parte General Tomo I. Bogotá D.C: Editorial Universidad Externado de Colombia.
Ruiz-Jarabo, D. (2005). El desarrollo comunitario del principio de precaución. En C. G. Judicial (Ed.). El principio de precaución y su proyección en el derecho administrativo español (pp. 41-74). Madrid: Centro de Documentación Judicial.
Unión Europea (2024). Reglamento de Inteligencia Artificial. Reglamento (Ue) 2024/1689 del Parlamento Europeo y del Consejo. https://eur-lex.europa.eu/legal-content/ES/TXT/PDF/?uri=OJ:L_202401689




