Inteligência artificial na análise de patologias corruptivas: delimitação jurisprudencial nas decisões do TCU do conceito aberto de cláusula restritiva ao caráter competitivo em editais de licitação
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2020-12-22
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Universidade Federal de Goiás
Resumo
In Brazil, the innovations brought by the new Civil Procedure Code (Law No. 13105/2015),
formalized the importance of using precedents at all levels in the Brazilian legal system. This
research arises from the need to congregate all decisions that deal with the same theme, in
contrast to simple keyword searches used these days, in order to provide greater legal
certainty. In administrative law, the Brazilian Federal Audit Court’s (TCU) decisions serves as
a guide for public actions, as they establish practical criteria that allow public managers to
conclude for a possible solution in a specific case. In this context, this empirical research had
as main objectives: (i) to create a replicable method of delineating jurisprudence and
identifying precedents with the use of Artificial Intelligence (AI) applicable, above all, to TCU
decisions that deal with clauses restricting the competitiveness in bidding notices; and (ii) to
contribute to the administrative precedents culture sedimentation and to the jurisprudential
delimitation of indeterminate legal concepts, especially those related to corrupt practices. The
area chosen for jurisprudential extraction application is found at the heart of frauds in public
bidding, that one of its corruption pathologies is the existence of clauses in bidding notices
that restrict the competitiveness in public purchases. Thus, this research tested AI techniques
(text mining for document classification) in the TCU decisions (between 1992 and 2018), with
the purpose of systematize the jurisprudential delimitation and consequent unambiguous
administrative precedents identification, which give concreteness to the concept of
competitiveness restrictive clauses. More than 300 judgments, previously labeled in 11
different classes related to bidding clauses considered restrictive to the competitive nature of
the public purchase, were used to train machine learning and deep learning models for multilabel classification, in order to verify whether the machine would be able to point out which
others TCU decisions were related to any of these classes. The results using convolutional
neural networks for the training and test phases proved to be reasonable, as they presented
an evaluation metric of 82.69%. However, in the supervised assessment stage, the trained
deep learning algorithm was inconclusive for the desired jurisprudential delimitation. Despite
the unsatisfactory results this research was successful, at least partially, in reaching its
general objectives, since the mapping of the computational state of art applied to the research
problem and the operational details described provide know-how transferring for futures
research that envisages the use of AI for jurisprudential systematization. It was also
demonstrated the importance of systematically tracking administrative precedents so that
there is greater legal security not only for public managers in their administrative actions, but
also in improving the algorithms used by AI applications that search for possible irregularities,
such as Alice the robot from TCU. Still, this study presents the construction of the
jurisprudential line with indication of the probable administrative precedents and their rationes
decidendi for the cases of clauses in bidding notices, considered restrictive because they
demand technical capacity from bidders in an irregular manner.
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Citação
SILVA, S. T. T. Inteligência artificial na análise de patologias corruptivas: delimitação jurisprudencial nas decisões do TCU do conceito aberto de cláusula restritiva ao caráter competitivo em editais de licitação. 2020. 197 f. Dissertação (Mestrado em Direito e Políticas Públicas) - Universidade Federal de Goiás, Goiânia, 2020.