Consequences and prospects of the application of Artificial Intelligence to criminal cases

Authors

  • Gustavo Mascarenhas Lacerda Pedrina Universidade de São Paulo (USP)

DOI:

https://doi.org/10.22197/rbdpp.v5i3.265

Keywords:

Artificial Intelligence, Statistical computation, Predictive analysis, Brain.

Abstract

This article aims to make an analysis of the current application and perspectives of what is popularly known by artificial intelligence to criminal law. Two aspects of machine learning are analyzed: statistical computation and predictive analysis. It discusses the consequences and alternatives for the use of artificial intelligence techniques taken the advances in the brain sciences, some relevant points on the topic are highlighted to promote discussions about future scenarios of this area and possible current applications.

Downloads

Download data is not yet available.

Author Biography

  • Gustavo Mascarenhas Lacerda Pedrina, Universidade de São Paulo (USP)
    Doutorando, Mestre e Bacharel em Direito pela Universidade de São Paulo (USP). Research Fellow no Charles Houston Institute da Harvard Law School (EUA). Organizador do livro AP: 470: análise da intervenção da mídia no julgamento do mensalão. Assessor de Ministro no Supremo Tribunal Federal. gumascarenhas@hotmail.com.

References

BARRET, Lisa Feldman. How emotions are made: the secret life of the brain. Boston: Houghton Mifflin Harcourt, 2017.

BERWICK, Robert C., CHOMSKY, Noam, Why only us: language and evolution. Cambridge: MIT Press, 2017.

CALISKAN-ISLAM, A. BRYSON, J.J, NARAYAAN, A. Semantics derived automatically from language corpora necessarily contain human biases. 2016. Princeton: Princeton University. Disponível em: http://randomwalker.info/publications/language-bias.pdf. Acesso em 19.04.2019.

FENOLL, Jordi Nieva, Inteligencia artificial y processo penal. Madrid: Marcial Pons, 2018.

KOTSIANTIS, S. B.; KANELLOPOULOS, D; PINTELAS, P. E. Data preprocessing for supervised learning. International Journal of Computer Science. Jornal digital. vol. 1, no. 2, 2006, p. 111-117.

MONAHAN, J; SKEEM, J. Risk Assessment in Criminal Sentencing. Virginia Public Law and Legal Theory Research Paper, n. 53. Disponível em: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2662082. Acesso em 19.07.2019.

RABOWITZ, N. C., PEBERT, F., SONG, H.F., ZHANG, C., ESLAMI, S.M.A., BOTVINIK, M., Machine Theory of Mind. Artigo digital: Cornell University, 2018. Disponível em https://arxiv.org/abs/1802.07740 Acesso em 16.07.2019.

STARR, S., Evidence-based sentencing and the scientific racionalization of discrimination. Stanford Law Review, Setembro de 2013. Disponível em: https://ssrn.com/abstract=2318940 Acesso em 16.07.2019

TURING, A. M., Computing machinery and intelligence. In: Mind, Vol. LIX, Outubro de 1950.

WINSTON, Patrick. The Strong Story Hypothesis and the Directed Perception Hypothesis. AAAI Fall Symposium Series, 2011. Artigo digital. Disponível em https://dspace.mit.edu/bitstream/handle/1721.1/67693/Submitted.pdf?sequence=1&isAllowed=y Acesso em 09.07.2019.

WINSTON, Patrick, HOLMES, Dylan, The Genesis enterprise: taking artificial intelligence to another level via computational account of human story understanding. Cambridge: MIT Computer Science and Artificial Intelligence Laboratory Center for Brains, Minds, and Machines, 2018.

YUDKOWSKY, E. The Ethics of Artificial Intelligence. In: FRANKISH, Keith; RAMSEY, William. The Cambridge Handbook of Artificial Intelligence. New York: Cambridge University Press, 2014.

Published

2019-10-31

Issue

Section

DOSSIÊ: Novas tecnologias e processo penal

How to Cite

Pedrina, G. M. L. (2019). Consequences and prospects of the application of Artificial Intelligence to criminal cases. Brazilian Journal of Criminal Procedure, 5(3), 1589-1606. https://doi.org/10.22197/rbdpp.v5i3.265