Jueces robot, dos aproximaciones prácticas a su concepto
DOI:
https://doi.org/10.51302/rtss.2024.20111Palabras clave:
juez robot, inteligencia artificial, Administración de justicia, resolución basada en reglas, aproximación autoadaptativa, codificación, predictibilidad, procesamiento del lenguaje natural, GPT-4Resumen
En la era digital actual, la posibilidad de integrar sistemas de inteligencia artificial (IA) en el ámbito jurídico ha suscitado un debate profundo sobre la administración automatizada de la justicia. Este artículo explora dos enfoques paradigmáticos en la conceptualización de un «juez robot»: el basado en reglas y el adaptativo. Mientras que el primero se centra en una codificación explícita de la ley, garantizando predictibilidad y transparencia, inspirado en AlphaZero, el segundo, inspirado en AlphaGo, se adapta continuamente a la jurisprudencia, ofreciendo flexibilidad y capacidad de evolución. A través de un análisis detallado, se discuten las ventajas, limitaciones y aplicaciones potenciales de ambos modelos. Asimismo, se muestran dos ejemplos concretos de juez robot basados en cada uno de los modelos, Python y Tkinter, para la resolución por IA de pleitos relativos a la extinción del contrato de trabajo por voluntad del trabajador fundamentados en retrasos en el abono de los salarios y en materia de revisión de incapacidades permanentes por mejoría.
Descargas
Citas
Almeida, G. F. C. F., Nunes, J. L., Engelmann, N., Wiegmann, A. y De Araújo, M. (2023). Exploring the psychology of GPT-4's Moral and Legal Reasoning. https://arxiv.org/abs/2308.01264v1
Ashley, K. D. (2017). Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press. https://doi.org/10.1017/9781316761380
Barocas, S. y Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671-732. https://www.jstor.org/stable/24758720
Branting, L. K. (2017). Data-centric and logic-based models for automated legal problem solving. Artificial Intelligence and Law, 25, 5-27. https://link.springer.com/article/10.1007/s10506-017-9193-x
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam. P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler. D. M., Wu, J., Winter, C.,… Amodei, D. (2020). Language models are few-shot learners. https://doi.org/10.48550/arXiv.2005.14165
Brownsword, R. (2008). Rights, Regulation, and the Technological Revolution. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199276806.001.0001
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. C. y Zhang, Y. (2023). Sparks of Artificial General Intelligence: Early experiments with GPT-4. https://doi.org/10.48550/arXiv.2303.12712
Chouldechova, A. y Roth, A. (2018). The frontiers of fairness in machine learning. https://doi.org/10.48550/arXiv.1810.08810
Dressel, J. y Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5777393/
Han, S. J., Ransom, K., Perfors, A. y Kemp, C. (2023). Inductive reasoning in humans and large language models. https://doi.org/10.48550/arXiv.2306.06548
Mutlu, B. y Forlizzi, J. (2008). Robots in organizations: The role of workflow, social, and environmental factors in human-robot interaction. HRI '08 Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, 287-294. https://dl.acm.org/doi/10.1145/1349822.1349860
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. y Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/205395171667967
Moor, J. H. (2006). The nature, importance, and difficulty of machine ethics. IEEE intelligent systems, 21, 18-21. https://philpapers.org/rec/MOOTNI
Nasri, H., Ouarda, W. y Alimi, A. M. (2016).ReLiDSS: Novel lie detection system from speech signal. IEEE/ACS 2016, 13.ª Conferencia Internacional de Sistemas y Aplicaciones Informáticas (AICCSA), 1-8. https://doi.org/10.1109/AICCSA.2016.7945789
Nori, H., King, N., McKinney, S. M., Carignan, D. y Horvitz, E. (2023). Capabilities of GPT-4 on Medical Challenge Problems. https://doi.org/10.48550/arXiv.2303.13375
Oswald, M., Grace, J., Urwin, S. y Barnes, G. C. (2018). Algorithmic risk assessment policing models: lessons from the Durham HART model and "Experimental" proportionality. Information & Communications Technology Law, 27(2), 223-250. https://doi.org/10.1080/13600834.2018.1458455
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Peng, B., Li, C., He, P., Galley, M. y Gao, J. (2023). Instruction Tuning with GPT-4. https://doi.org/10.48550/arXiv.2304.03277
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. y Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A. S…. Wellman, M. (2019). Machine behaviour. Nature, 568, 477-486. https://doi.org/10.1038/s41586-019-1138-y
Remus, D. y Levy, F. S. (2016). Can Robots Be Lawyers? Computers, Lawyers, and the Practice of Law. Geo. J. Legal Ethics, 30, 501-558. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2701092
Russell, S. J. y Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Prentice Hall. https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf
Sang-Hun, C. (15 de marzo de 2016). Google's Computer Program Beats Lee Se-dol in Go Tournament. https://www.nytimes.com/2016/03/16/world/asia/korea-alphago-vs-lee-sedol-go.html
Schraudolph, N. N., Dayan, P. y Sejnowski, T. J. (1994). Temporal Difference Learning of Position Evaluation in the Game of Go. https://www.researchgate.net/publication/2301607_Temporal_Difference_Learning_of_Position_Evaluation_in_the_Game_of_Go
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Kalchbrenner, N., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. y Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484-489. https://doi.org/10.1038/nature16961
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Panadero, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifré, L., Van Den Driessche, G., Graepel, T. y Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550, 354-359. https://doi.org/10.1038/nature24270
Surden, H. (2014). Machine learning and law. Washington Law Review, 89(1), 87-115. https://digitalcommons.law.uw.edu/wlr/vol89/iss1/5/
Susskind, R. (2019). Online Courts and the Future of Justice. Oxford University Press. https://global.oup.com/academic/product/online-courts-and-the-future-of-justice-9780192849304?cc=us&lang=en&
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. y Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Webb, T., Holyoak, K. J. y Lu, H. (2022). Emergent Analogical Reasoning in Large Language Models. https://doi.org/10.48550/arXiv.2212.09196
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J. y Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. https://doi.org/10.48550/arXiv.2302.11382
Zarsky, T. Z. (2013). Transparent predictions. University of Illinois Law Review, 4, 1.503-1.570. https://www.illinoislawreview.org/wp-content/ilr-content/articles/2013/4/Zarsky.pdf
