Inteligencia artificial y proceso penal

Autores/as

DOI:

https://doi.org/10.36097/rsan.v1i58.2808

Palabras clave:

crimen, decisiones judiciales, inteligencia artificial, proceso penal, seguridad

Resumen

La inteligencia artificial (IA) es una tecnología que ha revolucionado numerosos campos, incluyendo el proceso penal. En la actualidad, se están desarrollando sistemas de IA capaces de analizar grandes cantidades de datos, lo que puede ayudar a los tribunales a tomar decisiones más informadas y precisas. El presente artículo tiene como objetivo analizar el papel de la inteligencia artificial como herramienta de mejora y optimización de los procesos penales en Ecuador. A través de una metodología de revisión bibliográfica se recabó toda la información tendiente a entender el uso de la IA en el proceso penal para aumentar la eficiencia y eficacia del proceso penal, así como la identificación de patrones en grandes conjuntos de datos. Como resultado de la presente investigación, se pudieron evidenciar, entre otros, sistemas basados en IA para estudiar a los sospechosos más probables y a los patrones de delito para prevenir futuros delitos. Se evidenció que el uso de la IA en el proceso penal también plantea preocupaciones en cuanto a la privacidad y la seguridad de los datos, así como preguntas sobre cómo la IA afectaría la toma de decisiones judiciales, ya que algunos temen que los algoritmos de IA sean sesgados o discriminatorios.

Citas

Alarie, B., Niblett, A., & Yoon, A. H. (2016). How artificial intelligence will affect the practice of law. University of Toronto Law Journal, 66(2), 106-24. https://tspace.library.utoronto.ca/bitstream/1807/88092/1/Alarie%20Artificial%20Intelligence.pdf

Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., & Lampos, V. (2016). Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective. PeerJ Computer Science: https://peerj.com/articles/cs-93/

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

Babuta, A., Oswald, M., & Rinik, C. (2018). Machine Learning Algorithms and Police Decision-Making Legal, Ethical and Regulatory Challenges. Royal United Services Institute. https://n9.cl/1k8np

Bench-Capon, T., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values . Artificial Intelligence, 150(1-2), 97-143. https://doi.org/10.1016/S0004-3702(03)00108-5

Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2018). Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research, 50(3), 478-511. https://journals.sagepub.com/doi/10.1177/0049124118782533

Hernandez‑de‑Menendez, M., Morales‑Menendez R., Escobar C., Arinez J. (2019). Biometric applications in education. International Journal on Interactive Design and Manufacturing (IJIDeM) 15(2-3), 365-280. https://doi.org/10.1007/s12008-021-00760-6

Brayne, S. (2017). Big Data Surveillance: The Case of Policing. American Sociological Review 82(5), 977-1008. https://doi.org/10.1177/0003122417725865

Brynjolfsson, E., & McAfee, A. (2017). The Business of Artificial Intelligence. Harvard Business Review , 23-25. https://hbr.org/2017/07/the-business-of-artificial-intelligence

Carter, J., & Carter, D. L. (2019). Law enforcement fusion centers: Cultivating an information

sharing environment while safeguarding privacy. Journal of Police and Criminal Psychology 34 (1), 55-66. https://link.springer.com/article/10.1007/s11896-016-9199-4

Chainey, S., & Ratcliffe, J. (2013). GIS and Crime Mapping. John Wiley & Sons.

Chen, H., (2011). Dark Web: Exploring and Data Mining the Dark Side of the Web. Tuscon: Springer.

Chien, C. V. (2019). AI and IP: Innovation and research. Houston Law Review, 56(1), 19-36.

Cullen, F. T., Jonson, C. L., & Nagin, D. S. (2017). Prisons do not reduce recidivism: The high cost of ignoring science. The Prison Journal, 91(3_suppl), 48S-65S. DOI:10.1177/0032885511415224

Desmarais, S. L., Johnson, K. L., & Singh, J. P. (2016). Performance of recidivism risk

assessment instruments in U.S. correctional settings. Psychological Services, 13(3), 206-222. https://doi.org/10.1037/ser0000075

Dressel , J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1). https://www.science.org/doi/10.1126/sciadv.aao5580

Durlauf, S. N., & Nagin, D. S. (2011). Imprisonment and crime: Can both be reduced? Criminology & Public Policy, 10(1), 13-54. https://www.ojp.gov/ncjrs/virtual-library/abstracts/imprisonment-and-crime-can-both-be-reduced

Eaglin, J. M. (2017). Constructing recidivism risk. Emory Law Journal, 67(1), 59-118.

Fazel, S., Singh, J. P., Doll , H., & Grann, M. (2012). Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis. BMJ, 345. https://www.bmj.com/content/345/bmj.e4692

Ferguson, A. G. (2017). Policing predictive policing. Washington University Law Review, 1109-1189. https://journals.library.wustl.edu/lawreview/article/id/3851/

Garfinkel, S. L. (2010). Digital forensics research: The next 10 years. Digital Investigation 7,

S64-S73. https://doi.org/10.1016/j.diin.2010.05.009

Garvie, C., Bedoya, A., & Frankle, J. (2016). The perpetual lineup: Unregulated police face recognition in America. Georgetown Law Center on Privacy & Technology. https://www.perpetuallineup.org

Gates, K. A. (2011). Our Biometric Future Facial Recognition Technology and the Culture of Surveillance. NYU Press. https://nyupress.org/9780814732106/our-biometric-future/

Goodman-Delahunty, J., & Granhag, P. A. (2014). Psychological contributions to evaluating

evidence: A European perspective in Encyclopedia of criminology and criminal justice. Springer. https://doi.org/10.1177/174569161770651

Hung, T., Yen, C. (2023). Predictive policing and algorithmic fairness. Synthese, 201, 1-29. https://doi.org/10.1007/s11229-023-04189-0

Kim, J., Park S., Carriquiry, A. (2024). A deep learnig approach for the comparison of

handwritten documents using latent feature vectors. Statistical Analysis and Data Mining: The ASA Data Science Journal, 17, 1-19. https://doi.org/10.1002/sam.11660

Hunt, P., Saunders, J., & Hollywood, J. S. (2014). Evaluation of the Shreveport predictive policing experiment. RAND Corporation. https://www.rand.org/pubs/research_reports/RR531.html

Jain, A. K., Klare, B., & Park, U. (2011). Face recognition: Some challenges in forensics.

Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, 726-733. Obtenido de: https://www.rand.org/pubs/research_reports/RR531.html

Jung, J., Concannon, C., Shroff, R., Goel, S., & Goldstein, D. G. (2017). Simple rules for complex decisions. . Proceedings of the 2017 ACM Conference on Economics and Computation, 27-44. https://doi.org/10.48550/arXiv.1702.04690

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the

interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 15-25. https://doi.org/10.1016/j.bushor.2018.08.004

Katz, D. M. (2017). Quantitative legal prediction—or—How I learned to stop worrying and start preparing for the data-driven future of the legal services industry. . Emory Law Journal, 62, 909-966. Obtenido de: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2187752

Lipsey, M. W., Landenberger, N. A., & Wilson, S. J. (2010). Effects of cognitive-behavioral

programs for criminal offenders. Campbell Systematic Reviews, 6(1), 1-27. https://doi.org/10.4073/csr.2007.6

Shi, C., Chen, L.,Wang C., Zhou, X., & Qin, Z. (2023). Review on Image Forensic Techniques Based on Deep Learning . Preprints, 1-31. DOI:10.20944/preprints202306.1179.v1

Lum, K., & Isaac, W. (2016). To Predict and Serve? Significance, Volume 13, Issue, 14–19.

https://doi.org/10.1111/j.1740-9713.2016.00960.x

Frankenreiter, J., Nyarko, J. (2022). Natural Language Processing in Legal Tech, Legal Tech

and the Future of Civil Justice (David Engstrom ed.) Forthcoming. http://dx.doi.org/10.2139/ssrn.4027030 .

Meloy, J. R., & Hoffmann, J. (2014). International Handbook of Threat Assessment. New York: Oxford University Press.

Miremadi, M., Manyika , J., & Chui , M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly, 1-10. https://www.mckinsey.com/capabilities/quantumblack/our-insights/what-ai-can-and-cant-do-yet-for-your-business#/

Mohler, G. O., Short, M. B., Malinowski, S., Johnson, M., Tita, G. E., Bertozzi , A. L., &

Brantingham, P. J. (2015). Randomized controlled field trials of predictive policing. Journal of the American Statistical Association 110(512), 1399-1411. https://doi.org/10.1080/01621459.2015.1077710

Monahan, J., & Skeem, J. L. (2016). Risk assessment in criminal sentencing. Annual Review of Clinical Psychology, 12, 489-513. https://www.researchgate.net/publication/326450547_Risk_assessment_in_criminal_sentencing_Annual_Review_of_Clinical_Psychology_12_489-513_2016

Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., & Hollywood, J. S. (2013). Predictive

policing: The role of crime forecasting in law enforcement operations. RAND Corporation. Dhttps://doi.org/10.7249/RR233

Plamondon, R., & Lorette, G. (2013). The state of the art and future trends in computerized handwriting and document analysis. In Advances in handwriting and drawing: A multidisciplinary approach. (R. Plamondon, S. N. Srihari, & F. Leclerc, Edits.) World Scientific.

Pollard, K. A., & Rajaratna, D. (2011). Forensic image analysis in Handbook of digital imaging. (P. C. Wong, M. Liu, & M. F. Casares , Edits.). John Wiley & Sons.

Quick, D., & Choo, K.-K. R. (2014). Impacts of increasing volume of digital forensic data: A

survey and future research challenges. Digital Investigation 11(4), 273-294. https://doi.org/10.1016/j.diin.2014.09.002

Richardson, R., Schultz, J., & Crawford, K. (2019). Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice. New York University Law Review 94(1), 192-233. Obtenido de: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3333423

Rusell, S. J., & Norvig, P. (2009). Artificial intelligence: a modern approach. Prentice Hall.

Scrivens, R., Davies, G., & Frank, R. (2020). Measuring the evolution of radical right-wing

posting behaviors online. Deviant Behavior 41(2), 216-232. https://doi.org/10.1080/01639625.2018.1556994

Skeem, J. L., & Lowenkamp., C. T. (2016). Risk, race, & recidivism: Predictive bias and

disparate impact. Criminology, 54(4), 680-712. https://doi.org/10.1111/1745-9125.12123

Stevenson, M. (2018). Assessing risk assessment in action. Minnesota Law Review, 103(1), 303-

DOI: http://dx.doi.org/10.2139/ssrn.3016088

Surden, H. (2014). Machine learning and law. . Washington Law Review, 89(1), 87-115. Obtenido de: https://digitalcommons.law.uw.edu/wlr/vol89/iss1/5/

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the gap to human-

level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1701-1708. https://doi.org/10.1109/CVPR.2014.220

Tonry, M. (2017). Proportionate sentencing: Exploring the principles. Oxford University Press.

Viljoen, J. L., Cochrane, D. M., & Jonnson, M. R. (2018). Do risk assessment tools help manage

and reduce risk of violence and reoffending? A systematic review. Law and Human Behavior, 42(3), 181-214. https://psycnet.apa.org/doi/10.1037/lhb0000280

Wang, X., Gerber, M. S., & Brown, D. E. (2012). Automatic crime prediction using events extracted from Twitter posts. Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, 231-238. https://doi.org/10.1007/978-3-642-29047-3_28

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam: Morgan Kaufmann.

Zhao, W., Chellapa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature

survey. Publication History, 399-458. https://doi.org/10.1145/954339.954342

Zhao, Y., Wang, L., Huang, J., Wang, L., & Tan, T. (2018). Learning to predict charges for criminal cases with legal basis. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://aclanthology.org/D17-1289.pdf

Descargas

Publicado

2024-06-30

Cómo citar

García Falconí, R. J., & Barona Pazmiño, K. (2024). Inteligencia artificial y proceso penal. Revista San Gregorio, 1(58), 101–110. https://doi.org/10.36097/rsan.v1i58.2808

Número

Sección

ARTÍCULOS DE REVISIÓN