Detalhes bibliográficos
| Ano de defesa: |
2022 |
| Autor(a) principal: |
Moura, Lúcia Helena de Matos
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| Orientador(a): |
Silveira, Paulo Antonio Caliendo Velloso da
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| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
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| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Direito
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| Departamento: |
Escola de Direito
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| País: |
Brasil
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| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://tede2.pucrs.br/tede2/handle/tede/10679
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Resumo: |
The recovery of tax and non-tax credits of interest to the Union is carried out through tax enforcement action before the Federal Courts, Labor Courts and Electoral Courts. The National Council of Justice annually publishes the Justice in Numbers Report, which shows, repeatedly, the high rate of congestion of pending executions in the Judiciary, among these tax executions, considered the bottleneck. The main cause of this scenario is the difficulty faced by the tax enforcement courts in locating the debtor and the respective assets to be attached to settle the debts. Given this scenario, some courts of law decided to apply alternative solutions, and the path found was the development of Artificial Intelligence systems for the performance of predictive tasks. In view of the satisfactory results, the National Council of Justice disciplined this innovation through Resolution No. 395, of June 7, 2021. From there, the Electronic Judicial Process (PJE) was replacing the records in physical media and new intelligence systems Artificial were developed by State Courts of Justice. As an example, we have the Synapses System of the Court of Justice of Rondônia, which houses other Artificial Intelligence systems used in tax executions. The same experience must be used by the Federal Court. These are algorithmic platforms programmed to perform repetitive tasks whose results are achieved in seconds, compared to months. The use of Artificial Intelligence systems in use on algorithmic platforms are developed by the Machine Learning method - or Machine Learning -, in a supervised way, in order to achieve the satisfaction of public credits that, every year, reach the level billions that should reach the public coffers. Didactically, the functioning of these systems is shown in the form of introductory notes on Artificial Intelligence whose path starts with the respective origin until its application in judicial proceedings. The issue of efficiency, which is the main objective to be achieved in the Federal Court, is highlighted as a principle-norm and, therefore, necessary for due process of law. The use of Artificial Intelligence in Federal Justice in predictive acts shortens the turnaround time of tax executions and, thus, avoids the decree of intercurrent prescription. If a possible error occurs in the algorithms, it is programmed again, excluding its cause. It is concluded that it is possible to reduce the high rate of congestion of tax executions in the Federal Court from the use of algorithmic platforms programmed to locate the debtor and his assets, in compliance with the principle of human dignity. |
