Implementation of artificial intelligence in Bolivian law
Comparative analysis of Chat GPT and Gemini AI
DOI:
https://doi.org/10.59343/yuyay.v3i2.68Keywords:
artificial intelligence, legal prompting, digital law Bolivia, legaltechAbstract
Artificial intelligence has astonished the world with its advanced capabilities, facilitating task automation and significantly reducing the time required for previously laborious activities. This study aims to explore the implementation of artificial intelligence in the Bolivian legal field by comparing two tools: Chat GPT and Gemini AI. A literature review and practical case studies were conducted to evaluate both tools in three key areas: search and systematization of doctrine and jurisprudence, case analysis, and legal document drafting. The results demonstrate that both tools have the potential to improve efficiency and accuracy in the studied areas. However, some limitations were identified, mainly related to restricted access to information, which occasionally results in incorrect answers that require manual verification. In conclusion, although Chat GPT and Gemini AI present significant advantages for the legal field in Bolivia, it is essential to consider current limitations and the need for human supervision to ensure the accuracy of the results.
Downloads
References
Beltrán, N., & Rodríguez, E. (2021). Procesamiento del lenguaje natural (PLN) - GPT-3, y su aplicación en la ingeniería de software. TIA, 8(1), 18–37.
Cabello Ayzama, A., & Montesinos, S. (2020). De la cuarentena a la ineficacia de la garantía del derecho a la educación en el Estado Plurinacional de Bolivia: Un análisis sobre la vulneración del derecho a la educación desde el concepto de la posición jurídica definitiva. SCIAS Direitos Humanos e Educação, 3(2), 231–248.
CIDH. (2020). Resolución 1/2020: Pandemia y Derechos Humanos en las Américas.
Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., & Liu, R. (2019). Plug and Play Language Models: A Simple Approach to Controlled Text Generation. ICLR. http://arxiv.org/abs/1912.02164
Gutiérrez, J. (2023, January 27). En sólo 5 días, Chat GPT-3 consiguió un millón de usuarios. Periódico La Jornada, 14.
Jiang, C., & Yang, X. (2023). Legal Syllogism Prompting: Teaching large language models for legal judgment prediction. Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, 417–421. https://doi.org/10.1145/3594536.3595170
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys, 55(9), 1–35. https://doi.org/10.1145/3560815
Lopezosa, C. (2023). La Inteligencia artificial generativa en la comunicación científica: retos y oportunidades. Revista de Investigación e Innovación En Ciencias de La Salud, 5(1), 1–5. https://doi.org/10.46634/riics.211
Lopezosa, C., & Codina, L. (2023). Inteligencia Artificial y métodos cualitativos de investigación: ChatGPT para revisiones de la literatura y análisis de entrevistas semiestructuras.
Martínez, L. A. (2021). Brechas digitales y derecho a la educación durante la pandemia por COVID-19. Propuesta Educativa FLACSO, 2(56), 11–27.
Morales-Chan, M. A. (2023). Explorando el potencial de Chat GPT: Una clasificación de Prompts efectivos para la enseñanza.
Navarro, J., Medina, O., Andrade, C., Chacín, M., & Bermudez, V. (2020). Cultura de paz en la pandemia por SARS-COV-2: confinamiento, tics y las redes sociales. Gaceta Médica de Caracas, 128(S2), S350–S358. https://doi.org/10.47307/GMC.2020.128.s2.21
Surden, H. (2024). ChatGPT, AI large language models, and law. Fordham Law Review, 92, 1941–1972.
Torres Molina, K., & Blanco Pérez, I. (2023). Arquitectura de prompt académicos para el uso de inteligencias artificiales (AI) en Areandina: desafíos, experiencias y obstáculos. Revista Investigaciones Andina, 25(46). https://doi.org/10.33132/01248146.2258
Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–459.
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Computational Intelligence Magazine, 13(3), 55–75. https://doi.org/10.1109/MCI.2018.2840738
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2024 Alex Cabello-Ayzama
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
Share — copy and redistribute material in any medium or format.
- Licensor cannot revoke these freedoms if you follow the terms of the license.
Attribution — You must give credit appropriately, provide a link to the license, and indicate if any changes have been made. You may do so in any reasonable way, but not in a manner that suggests that you or your use are supported by the licensor.
NonCommercial — You may not use the material for commercial purposes.
NonDerivatives — If you remix, transform, or create from the material , you may not distribute the modified material.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from making any use permitted by the license.
