Online first
Artículos

Inteligencia Artificial Generativa (IAGEN) y defensa: ¿Cómo lograr la "convicción" de una máquina?

PDF HTML
  • Jordi Nieva Fenoll

Publicado 2025-12-15

Palabras clave

  • marco mental,
  • heurístico,
  • prompt,
  • proceso judicial

Derechos de autor 2025 Jordi Nieva Fenoll

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

Cómo citar

Inteligencia Artificial Generativa (IAGEN) y defensa: ¿Cómo lograr la "convicción" de una máquina?. (2025). Revista Ítalo-española De Derecho Procesal, 1-17. https://doi.org/10.37417/rivitsproc/3204

Resumen

Hasta el momento, la defensa en los procesos ha estado centrada, más allá de en el estudio jurídico, en la elaboración de un relato que pudiera persuadir emocionalmente al juez, creándole un marco mental. Esa estrategia dejaría de tener sentido si el juez es sustituido por una herramienta de inteligencia artificial. Con todo, existen estrategias similares a la citada que todavía podrían seguirse en ese escenario, aunque teniendo en cuenta un trasfondo que nada tiene que ver con la experiencia del juez ni con sus emociones, sino con la consciencia de cómo funciona la inteligencia artificial generativa.

PDF HTML

Descargas

Los datos de descarga todavía no están disponibles.

Referencias

  1. Allen, R.J.; Pardo, M.S., Relative Plausibility and Its Critics, International Journal of Evidence & Proof, vol. 23, no. 1-2, April 2019, p. 21.
  2. Andrés Ibáñez, P., El tercero en discordia, Madrid 2015.
  3. Andrés-Pueyo, A., Arbach-Lucioni, K., Redondo, S., The RisCanvi: A New Tool for Assessing Risk for Violence in Prison and Recidivism, en Handbook of Recidivism Risk/Needs Assessment Tools, Chichester 2018, pp. 255 y ss.
  4. Andrews, A.; Clawson, R.A.; Gramig, B.M., Finding the Right Value: Framing Effects on Domain Experts, Political Psychology, vol. 38, 2, Abril 2017, pp. 261 y ss.
  5. Assmann, J., Ma‘at. Gerechtigkeit und Unsterblichkeit im Alten Ägypten, München 1995.
  6. Azón, Summa Azonis, Lib. IV, 19 (de probationibus). punto 21, p. 318.
  7. Bentham, J., Traité des preuves judiciaires, Paris 1823,
  8. Bornstein, A. M., Are algorithms building the new infrastructure of racism?, Nautilus, 21-12-2017, http://nautil.us/issue/55/trust/are-algorithms-building-the-new-infrastructure-of-racism.
  9. Bostanci, M., Yilmaz, Ö, Labour and exploitation processes in artificial intelligence: example of digital taylorism in data labelling, Toplum ve Kültür, Journal of Social & Cultural Studies, 15-05-2025.
  10. Brandom, R., Mechanical Turkers may have out-predicted the most popular crime-predicting algorithm, The Verge, 17-1-2018, https://www.theverge.com/2018/1/17/16902016/compas-algorithm-sentencing-court-accuracy-problem.
  11. Carroll, M.W., Copyright and the Progress of Science: Why Text and Data Mining Is Lawful, UC Davis Law Review, 53, 2019-2020, pp. 899 y ss.
  12. Cuzzolin F, Morelli A, Cîrstea B, Sahakian BJ. Knowing me, knowing you: theory of mind in AI, Psychological Medicine. 2020, 50, 7, pp. 1057 y ss.
  13. Degli Ubaldi, Baldo, Practica Baldi, 1521, folio 39 vuelto. Jaumar Carrera, Práctica forense, 1840, pp. 39-45.
  14. Descartes, R., Discurso del método, Madrid 1986, p. 35.
  15. Druckman, J. N., Political Preference Formation: Competition, Deliberation, and the (Ir)relevance of Framing Effects, American Political Science Review, nov. 2004, vol. 98, 4, pp. 671 y ss.
  16. Dubois, J., Adolphs, R., Neuropsychology: How Many Emotions Are There?, Current Biology. A Cell Press journal, 2015, vol. 25, 15,.
  17. Engel, C., Linhardt, L., Schubert, M., Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism, Artificial Intelligence and Law, 2025, pp. 383 y ss.
  18. Forza, Menegon, Rumiati, Il giudice emotivo, Bologna 2017.
  19. Fournier, L. R., “The Daubert Guidelines: Usefulness, Utilization, and Suggestions for Improving Quality Control”, Journal of Applied Research in Memory and Cognition, 5, 2016, p. 308.
  20. Furnham, A.; Chu Boo, H., A literature review of the anchoring effect, The Journal of Socio-Economics, vol 40, 1, Feb. 2011, pp. 35 y ss.
  21. Fussell, S., Study Finds Crime-Predicting Algorithm Is No Smarter Than Online Poll Takers, Gizmodo, 18-1-2018, https://gizmodo.com/study-finds-crime-predicting-algorithm-is-no-smarter-th-1822173965.
  22. Golden, W., ChatGPT: A trusted source?, Irish Journal of Technology Enhanced Learning, vol. 7, 2, pp. 113 y ss.
  23. Gregorcic, B., Pendrill, A,-M., ChatGPT and the frustrated Socrates, Physical Education, 2023, pp. 1 y ss.
  24. Guimarães, S., Moura, D., Vascular adrenoceptors: an update, Pharmacological Reviews, vol. 53, n. 2, pp. 319 y ss. Hoffman, B.B., Adrenaline, London 2013, p. 1.
  25. Haddad, R., Mercier-Laurent, E., Curriculum Vitae (CVs) Evaluation Using Machine Learning Approach, en Mercier-Laurent, E., Kayalica, M.Ö., Owoc, M.L. (eds), Artificial Intelligence for Knowledge Management. AI4KM 2021. IFIP Advances in Information and Communication Technology, vol. 614. Springer 2021.
  26. Hicks, M.T., Humphries, J., Slater, J., ChatGPT is bullshit, Ethics and Information Technology, 2024, p. 38.
  27. Hülshoff, T., Emotionen, München 2006.
  28. Julià Pijoan, M., La computerización del derecho, a partir del proceso y de los procedimientos judiciales, Madrid 2024.
  29. Julià Pijoan, M., RisCanvi. ¿Un protocolo de discriminación algorítmica?, en AAVV (coord.. Bueno de Mata), Justicia y derecho en datos, 2023, pp. 123 y ss.
  30. Khosrowi, D., Van Basshuysen, P., Making a Murderer: How Risk Assessment Tools May Produce Rather Than Predict Criminal Behavior, American Philosophical Quarterly, 2024, 61 (4), pp. 309 y ss. https://scholarlypublishingcollective.org/uip/apq/article/61/4/309/391697
  31. Kim, T., Song, H, The Effect of Message Framing and Timing on the Acceptance of Artificial Intelligence’s Suggestion, Association for Computing Machinery, 2020.
  32. Laizure, S. C., Caution: ChatGPT Doesn’t Know What You Are Asking and Doesn’t Know What It Is Saying, The Journal of Pedriatic Pharmacology and Therapeutics, 14-10-2024, https://jppt.kglmeridian.com/view/journals/jppt/29/5/article-p558.xml.
  33. Langley, C., Cirstea, B.I., Cuzzolin, F., Sahakian, B., Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review, Frontiers in Artificial Intelligence,Vol. 5, 2022.
  34. Larson, D. A., Artificial Intelligence: Robots, Avatars, and the Demise of the Human Mediator, Ohio State Journal on Dispute Resolution, Vol. 25, 2010, pp. 117-118.
  35. Larson, J., Mattu, S., Kirchner, L., Angwin, J, How We Analyzed The Compas Recidivism Algorithm, Propublica, 23-5-2016, https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm.
  36. Liu, P., Yuan, W., Fu, Y., Jiang, Z., Hayashi, H., Neubig, G., Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, ACM Computing Surveys, Volume 55, 9, n. 195, pp. 1 y ss.
  37. Manzanero, A. L., Psicología del testimonio, Madrid 2008, pp. 27 y ss.
  38. Metallo, V.N.A., The Impact of Artificial Intelligence on Forensic Accounting and Testimony - Congress Should Amend “the Daubert Rule” to Include a New Standard, Emory Law Journal Online, 69, 2019-2020, pp. 2039 y ss.
  39. Myers, D. G., Intuición. El poder y el peligro del sexto sentido, trad. de Guillermo Solana de Intuition: its power and perils, New Haven y Londres 2002, Barcelona 2003.
  40. Nieva Fenoll, J. Inteligencia artificial y proceso, Madrid 2018.
  41. Nieva Fenoll, J. Trasfondo psicológico de la actividad judicial, en Hacia el proceso judicial del siglo XXII, Valencia 2025, pp. 213 y ss.
  42. Nissan, E., “Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement”, AI & Society, 2015, p. 17.
  43. Northpointe, Practioners Guide to COMPAS, 17-8-2012.
  44. Passanante, L., Il precedente impossibile, Torino 2018, pp. 273 y ss.
  45. Plous, S., The psychology of judgment and decision making, 1993.
  46. Prakken, H., “Modelling Reasoning about Evidence in Legal Procedure”, ICAIL 2001, pp. 119 y ss.
  47. Quattrocolo, S., Arificial Intelligence, Computational Modelling and Criminal Proceedings, Springer 2020.
  48. Ran. Xi, Systems approach to shedding sunlight on A.I. black boxes, Hofstra Law Review, 53, 2, 2025, pp. 451 y ss.
  49. Rani, U., Dhir, R.K., The Artificial Intelligence illusion: How invisible workers fuel the “automated” economy, International Labour Organisation. 10-12-2024, https://www.ilo.org/resource/article/artificial-intelligence-illusion-how-invisible-workers-fuel-automated.
  50. Seymour, W., Detecting Bias: Does an Algorithm Have to Be Transparent in Order to Be Fair?, 2018, https://ir.shef.ac.uk/bias/pdf/seymour2018detecting.pdf
  51. Tarr, G. A., Without fear or favor. Judicial independence and judicial accountability in the States, Stanford 2012, p. 91.
  52. Tashea, J., Risk-Assessment Algorithms Challenged In Bail, Sentencing And Parole Decisions, ABA Journal, Marzo 2017, http://www.abajournal.com/magazine/article/algorithm_bail_sentencing_parole.
  53. Tversky, A., K., Daniel, The Framing of decisions and the psychology of choice, Science, 211, 1981, (4481), pp. 453 y ss.
  54. Tversky, A., Kahneman, D., Availability: A heuristic for judging frequency and probability, en Kahneman, Slovic, Tversky (ed.), Judgment under Uncertainty: Heuristics and Biases, cit., pp. 163 y ss.
  55. Tversky, A., Kahneman, D., Judgments of and by Representativeness, en Kahneman, Slovic, Tversky (ed.). Judgment under Uncertainty: Heuristics and Biases, pp. 84 y ss.
  56. Tversky, A.. Kahneman, D., Judgment under Uncertainty: Heuristics and Biases, Science, New Series, Vol. 185, n. 4157, Sep. 27, 1974, p. 1128.

Update cookies preferences