Technical, Epistemological, and Legal Divergences in the Automated Assessment of Testimonial Credibility through Artificial Intelligence in Ecuadorian Criminal Proceedings
DOI:
https://doi.org/10.70577/asce.v5i3.1031Keywords:
assessment of testimony; artificial intelligence; deception detection; procedural guarantees; algorithmic bias; Ecuador.Abstract
The incorporation of artificial intelligence (AI) into Latin American justice systems produced one of its most contentious applications: the autonomous assessment of testimonial credibility through systems that seek to infer a declarant's internal states, such as sincerity, coherence, or reliability, from verbal, vocal, or behavioral markers, within Ecuadorian criminal proceedings. This article examined the limits of that aspiration and determined under what conditions, if any, it proved compatible with due-process guarantees in Ecuador. A scoping review guided by PRISMA-ScR, complemented with dogmatic legal analysis, integrated forensic psychology, computer science, and criminal procedural law across 43 sources selected under explicit inclusion and quality criteria. The results revealed three converging clusters of constraints: technical (fragile ground truth, training biases, poor transferability, opacity of deep models, and the base-rate problem), epistemological (credibility, veracity, accuracy, and reliability proved non-equivalent, and behavioral cues to deception proved intrinsically weak), and legal (incompatibility with immediacy, cross-examination, reasoned motivation, and equality). Under current conditions of technological development, the use of artificial intelligence systems to independently determine the credibility of testimony did not prove fully compatible with the due-process guarantees established in the Constitution of the Republic of Ecuador. Building on these findings, the study proposed a framework for the responsible use of artificial intelligence that limited its application to supporting and complementary tasks, and outlined a three-stage admissibility protocol designed to integrate with the COIP, the LOPDP, and the criteria of the Inter-American Human Rights System.
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References
Asamblea Constituyente del Ecuador. (2008). Constitución de la República del Ecuador. Registro Oficial No. 449, 20 de octubre de 2008. https://www.finanzas.gob.ec/constitucion-de-la-republica/
Asamblea Nacional del Ecuador. (2014). Código Orgánico Integral Penal (COIP). Registro Oficial Suplemento No. 180, 10 de febrero de 2014.
Asamblea Nacional del Ecuador. (2021). Ley Orgánica de Protección de Datos Personales (LOPDP). Registro Oficial Suplemento No. 459, 26 de mayo de 2021.
Boix Palop, A. (2020). Los algoritmos son reglamentos: La necesidad de extender las garantías propias de las normas reglamentarias a los programas empleados por la administración para la adopción de decisiones. Revista de Derecho Público: Teoría y Método, 1, 223–269. https://doi.org/10.37417/RPD/vol_1_2020_33 DOI: https://doi.org/10.37417/RPD/vol_1_2020_33
Bogaard, G., Meijer, E. H., Vrij, A., & Nahari, G. (2022). Detecting deception using comparable truth baselines. Psychology, Crime & Law, 29(6), 567–583. https://doi.org/10.1080/1068316X.2022.2030334 DOI: https://doi.org/10.1080/1068316X.2022.2030334
Burrell, J. (2016). How the machine "thinks": Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512 DOI: https://doi.org/10.1177/2053951715622512
Cerrillo i Martínez, A., Di Lascio, F., Martín Delgado, I., & Velasco Rico, C. I. (Dirs.). (2022). Inteligencia artificial y administraciones públicas: Una triple visión en clave comparada. Marcial Pons.
Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. DOI: https://doi.org/10.12987/9780300252392
Denault, V., Plusquellec, P., Jupe, L. M., St-Yves, M., Dunbar, N. E., Hartwig, M., Sporer, S. L., Rioux-Turcotte, J., Jarry, J., Walsh, D., Otgaar, H., Viziteu, A., Talwar, V., Keatley, D. A., Blandón-Gitlin, I., Townson, C., Deslauriers-Varin, N., Lilienfeld, S. O., Patterson, M. L., … van Koppen, P. J. (2020). The analysis of nonverbal communication: The dangers of pseudoscience in security and justice contexts. Anuario de Psicología Jurídica, 30(1), 1–12. https://doi.org/10.5093/apj2019a9 DOI: https://doi.org/10.5093/apj2019a9
DePaulo, B. M., Lindsay, J. J., Malone, B. E., Muhlenbruck, L., Charlton, K., & Cooper, H. (2003). Cues to deception. Psychological Bulletin, 129(1), 74–118. https://doi.org/10.1037/0033-2909.129.1.74 DOI: https://doi.org/10.1037/0033-2909.129.1.74
Estevez, E., Linares Lejarraga, S., & Fillottrani, P. (2020). PROMETEA: Transformando la administración de justicia con herramientas de inteligencia artificial. Banco Interamericano de Desarrollo. DOI: https://doi.org/10.18235/0002378
Ferrer Beltrán, J. (2007). La valoración racional de la prueba. Marcial Pons.
Gascón Abellán, M. (2010). Los hechos en el Derecho: Bases argumentales de la prueba (3.ª ed.). Marcial Pons. DOI: https://doi.org/10.2307/jj.2322004
González, J. L., & Manzanero, A. L. (2018). Obtención y valoración del testimonio: Protocolo holístico de evaluación de la prueba testifical (HELPT). Pirámide. DOI: https://doi.org/10.2307/j.ctvwcjgg1.11
Hartwig, M., & Bond, C. F., Jr. (2011). Why do lie-catchers fail? A lens model meta-analysis of human lie judgments. Psychological Bulletin, 137(4), 643–659. https://doi.org/10.1037/a0023589 DOI: https://doi.org/10.1037/a0023589
Kordzadeh, N., & Ghasemaghaei, M. (2022). Algorithmic bias: Review, synthesis, and future research directions. European Journal of Information Systems, 31(3), 388–409. https://doi.org/10.1080/0960085X.2021.1927212 DOI: https://doi.org/10.1080/0960085X.2021.1927212
Laudan, L. (2006). Truth, error, and criminal law: An essay in legal epistemology. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511617515
Manzanero, A. L. (2010). Memoria de testigos: Obtención y valoración de la prueba testifical. Pirámide.
Markowitz, D. M., Hancock, J. T., & Bailenson, J. N. (2024). Linguistic markers of inherently false AI communication and intentionally false human communication: Evidence from hotel reviews. Journal of Language and Social Psychology, 43(1), 63–82. https://doi.org/10.1177/0261927X231200201 DOI: https://doi.org/10.1177/0261927X231200201
Oberlader, V. A., Quinten, L., Banse, R., Volbert, R., Schmidt, A. F., & Schönbrodt, F. D. (2021). Validity of content-based techniques for credibility assessment—How telling is an extended meta-analysis taking research bias into account? Applied Cognitive Psychology, 35(2), 393–410. https://doi.org/10.1002/acp.3776 DOI: https://doi.org/10.1002/acp.3776
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. DOI: https://doi.org/10.4159/harvard.9780674736061
Pérez-Pacheco, Y. (2025). Regulación de la IA en el proceso judicial: Desafíos y oportunidades en América Latina. Revista Especializada en Investigación Jurídica, 9(16), 1–25. https://doi.org/10.20983/reij.2025.1.1 DOI: https://doi.org/10.20983/reij.2025.1.1
Pérez-Rosas, V., Abouelenien, M., Mihalcea, R., & Burzo, M. (2015). Deception detection using real-life trial data. Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ICMI '15), 59–66. https://doi.org/10.1145/2818346.2820758 DOI: https://doi.org/10.1145/2818346.2820758
Reglamento (UE) 2024/1689 del Parlamento Europeo y del Consejo, de 13 de junio de 2024, por el que se establecen normas armonizadas en materia de inteligencia artificial (Reglamento de Inteligencia Artificial). (2024). Diario Oficial de la Unión Europea. http://data.europa.eu/eli/reg/2024/1689/oj
Roth, A. (2017). Machine testimony. Yale Law Journal, 126(7), 1972–2259.
Rudin, C., Wang, C., & Coker, B. (2020). The age of secrecy and unfairness in recidivism prediction. Harvard Data Science Review, 2(1). https://doi.org/10.1162/99608f92.6ed64b30 DOI: https://doi.org/10.1162/99608f92.6ed64b30
Saavedra Rionda, V. P., & Upegui Mejía, J. C. (2021). PretorIA y la automatización del procesamiento de causas de derechos humanos. Dejusticia. DOI: https://doi.org/10.51438/DJsaavedra2021a
Sánchez-Monedero, J., & Dencik, L. (2022). The politics of deceptive borders: "Biomarkers of deceit" and the case of iBorderCtrl. Information, Communication & Society, 25(3), 413–430. https://doi.org/10.1080/1369118X.2020.1792530 DOI: https://doi.org/10.1080/1369118X.2020.1792530
Segura, R. E. (2023). Inteligencia artificial y administración de justicia: Desafíos derivados del contexto latinoamericano. Revista de Bioética y Derecho, 58, 45–72. https://doi.org/10.1344/rbd2023.58.40601 DOI: https://doi.org/10.1344/rbd2023.58.40601
Steller, M., & Köhnken, G. (1989). Criteria-based statement analysis. En D. C. Raskin (Ed.), Psychological methods in criminal investigation and evidence (pp. 217–245). Springer.
Suchotzki, K., & Gamer, M. (2024). Detecting deception with artificial intelligence: Promises and perils. Trends in Cognitive Sciences, 28(6), 481–483. https://doi.org/10.1016/j.tics.2024.04.002 DOI: https://doi.org/10.1016/j.tics.2024.04.002
Taruffo, M. (2002). La prueba de los hechos (J. Ferrer Beltrán, Trad.). Marcial Pons. (Obra original publicada en 1992).
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850 DOI: https://doi.org/10.7326/M18-0850
UNESCO. (2021). Recomendación sobre la ética de la inteligencia artificial. Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura. https://unesdoc.unesco.org/ark:/48223/pf0000381137_spa
Vrij, A., Granhag, P. A., Ashkenazi, T., Ganis, G., Leal, S., & Fisher, R. P. (2022). Verbal lie detection: Its past, present and future. Brain Sciences, 12(12), 1644. https://doi.org/10.3390/brainsci12121644 DOI: https://doi.org/10.3390/brainsci12121644
Wachter, S., Mittelstadt, B., & Russell, C. (2018). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887. https://doi.org/10.2139/ssrn.3063289 DOI: https://doi.org/10.2139/ssrn.3063289
Wang, C., Han, B., Patel, B., & Rudin, C. (2023). In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction. Journal of Quantitative Criminology, 39(2), 519–581. https://doi.org/10.1007/s10940-022-09545-w DOI: https://doi.org/10.1007/s10940-022-09545-w
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