Mulheres artificiais contra a corrupção: em busca de legitimidade no Tribunal de Contas da União

Este estudo descreve a busca pela legitimidade de quatro artefatos de tecnologia da informação para auxiliar auditores na vigilância contra fraude e corrupção no Tribunal de Contas da União (TCU). ALICE, ADELE, MONICA e SOFIA são sistemas de Inteligência Artificial (IA) propostos para auxiliar os processos de auditoria no setor público. Um questionário online foi utilizado para reunir as respostas de 60 auditores de todo o Brasil, com entrevistas semiestruturadas com o Diretor de Dados, três desenvolvedores de TI e cinco gerentes de auditoria do TCU selecionados por amostragem intencional (purposive sampling). A pesquisa demonstra que o uso dos sistemas baseados em IA é baixo entre os auditores do TCU devido a um limitado benefício percebido. Embora alguns respondentes reconheçam as vantagens dos sistemas baseados em IA, o seu uso é adiado por uma fraca teorização e difusão em relação ao significado e ao uso desses sistemas dentro da organização; os auditores mostraram uma prioridade no uso dos métodos tradicionais de auditoria em detrimento da inovação digital, restringindo o potencial de controle do uso dos artefatos tecnológicos contra a corrupção.This study depicts the search for legitimacy by four information technology artifacts in helping auditors in the surveillance against fraud and corruption by the Brazilian Supreme Audit Institution (TCU). ALICE, ADELE, MONICA, and SOFIA are Artificial Intelligence (AI) systems proposed to aid auditing processes in the public sector. A web-based survey has been used to gather the responses from 60 auditors across Brazil and semi-structured interviews with the Chief Data Officer, three IT Developers and five TCU Audit Managers selected by purposive sampling. The research finds that the use of AI-based systems is low among auditors at the TCU due to the perceived limited benefit. While some respondents recognized the advantages of the AI-based systems, they are put off by the weak theorization and diffusion regarding the meaning and the use of AI-based systems within the organization; they showed a priority for using traditional auditing methods instead of digital innovation, restricting the potential of anticorruption control by technological artifacts

Implementing E-government Processes Distribution with Transparency using Multi-Agent Systems

E-government processes need transparency in order to allow citizens to understand and access valuable information in a democratic society. In this article, we present a multi-agent system (MAS) to process distribution that implements transparency characteristics. We demonstrate that the MAS paradigm stresses the organizational operating environment and the information systems alignment, being adequate to maintain process transparency. An agent-oriented software development methodology is used to define the soft goals of agents according to Tropos. The MAS architecture and the prototype were defined, implemented and illustrated with lawsuits distribution data from the Superior Labor Court of Brazil

Deep Vacuity : detecção e classificação automática de padrões com risco de conluio em dados públicos de licitações de obras

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2021.A identificação de fraudes e conluios em licitações de obras públicas é uma tarefa man- ual dispendiosa dependente tanto de experiência profissional quanto de profundo conheci- mento técnico e legal. As bases de dados públicas, aliadas a dados de licitações e contratos previamente analisados por peritos criminais altamente capacitados, formaram a base de dados passível de ser analisada para a identificação de atos ilícitos. Neste trabalho é pro- posta uma metodologia para realizar a detecção e classificação automática de padrões de conluio em licitações públicas, utilizando como fontes os dados disponíveis nos principais repositórios oficiais públicos, agregando a utilização de técnicas de reconhecimento de padrões para a realização deste objetivo proposto. Em uma abordagem inicial, obteve-se com sucesso para a formação da base de dados do trabalho um total de 15.132.968 pub- licações da Seção 3 do Diário Oficial da União em formato de texto e 1.907 documentos como referência de indicativo de atividades de conluio (estes disponibilizados por institu- ição parceira) que indicavam risco no processo licitatório. Foram testados modelos lineares clássicos, redes neurais profundas, bottleneck, Bi-LSTM e multicanal com vetorização do texto com TF-IDF e DOC2VEC, e dados estruturados extraídos do texto. O melhor F1- score foi obtido com o modelo passive-aggressive com 93,4% e o modelo bottleneck obteve 93,0% com melhor precisão.Identifying fraud and collusion in public bids is an expensive manual task and de- pendent on professional experience using in-depth technical and legal knowledge. Public databases, allied to bidding and contract data previously analyzed by highly trained crim- inal experts, form the database that can be analyzed for irregularities identification. This work proposes a methodology for automatic detection and classification of collusion pat- terns in public bids text, using data sources available on main public official repositories and adding pattern recognition techniques to achieve a model that detects and classifies this pattern. In an initial approach, a total of 15, 132, 968 publications of the Diario Oficial da União news, Section 3, in text format and 1, 907 documents as a reference for collusion activities were successfully obtained for the formation of the central work database (pro- vided by a partner institution) that indicated risk in the bidding process. Classic linear models, deep neural networks, bottleneck, Bi-LSTM, and multichannel were tested with text vectorization with TF-IDF and DOC2VEC, and structured data extracted from the text. The best F1-score was obtained with a passive-aggressive model with 93.4%, but the bottleneck model obtained 93.0% with better precision

Nuclear fuel assurance : origins, trends, and policy issues

The economic, technical and political issues which bear on the security of nuclear fuel supply interna- tionally are addressed. The structure of international markets for nuclear fuel is delineated; this includes an analysis of the political constraints on fuel availability, especially the connection to supplier nonproliferation policies. The historical development of nuclear fuel assurance problems is explored and and assessment is made of future trends in supply and demand and in the political context in which fuel trade will take place in the future. Finally, key events and policies which will affect future assurance are identified.Prepared for the U.S. Dept. of Energy through Associated Universities, inc., Contract no. 426365-S

PRICE-FIXING OVERCHARGES: LEGAL AND ECONOMIC EVIDENCE

This paper surveys hundreds of published social-science studies of private, hard-core cartels that contain 699 observations of long-run overcharges. The primary finding is that the median cartel overcharge for all types of cartels over all time periods is 25%: 19% for domestic cartels, 32% for international cartels, and 31% for all successful cartels. Thus, international cartels have historically been about 68% more effective in raising prices than domestic cartels. Cartel overcharges are skewed to the high side, pushing the mean overcharge for all types of cartels over all time periods to 42%. "Peak" cartel overcharges are typically double those of the long-run averages. These results are generally consistent with the few, more limited, previously published works that survey cartel overcharges. There is no evidence that convicted cartels are markedly less effective than unpunished ones. The results of a second survey of final verdicts in decided U.S. horizontal collusion cases, only three of which were international cartels, show an average median overcharge of 21% and an average mean overcharge of 30%. Outside the United States, 62 decisions of competition commissions cited median average overcharges of 25% and a mean of 47%. There are three significant policy implications. First, there is a view among some antitrust writers that there is little evidence that cartels raise prices significantly for a period long enough to justify the height of current U.S. cartel penalties. This survey's results, which are based upon an extraordinarily large amount of data spanning a broad swath of history of all types of private cartels, sharply contradict these views. In fact, the data suggest that U.S. penalties ought to be increased. Mean overcharges are three times as high as the level presumed by the U.S. Sentencing Commission. Surprisingly, bid rigging was no more injurious than other forms of collusion, which suggests that the USSC should amend its Guidelines that currently treat bid rigging more harshly than other forms of collusion. Second, the principal antitrust authorities abroad often base their typical or maximum fines on a 10% harm presumption. Average fines imposed since 1995 by Canada and the EU on identical cartels have been lower than U.S. government fines, yet overcharges generated by cartels discovered outside the United States are higher than North America-centered cartels. Consequently, anticartel laws and fine-setting practices abroad are in even greater need of strengthening. Third, cartels with multi-continental price effects are the most harmful type. Despite the evident increases in cartel detection rates and the size of monetary fines and penalties in the past decade, a good case can be made that current global anticartel regimes are under-deterring. While the recent worldwide trend towards the intensification of cartel penalties has been desirable, global cartels are more difficult to detect, have less fear from entry of rivals, achieve higher levels of sales and profitability, and systematically receive weaker corporate sanctions than comparable domestic cartels. Antitrust sanctions worldwide should be higher for global cartels than for other types.International Relations/Trade,

Aplicação do algoritmo Apriori para detectar relacionamentos entre empresas nos processos licitatórios do Governo Federal

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.A mineração de dados tem sido uma área de alta visibilidade nos últimos anos e de muitas pesquisas que mostraram boa eficiência dessa área para encontrar informações em grandes bases de dados. Esse trabalho propõe usar a mineração de dados nas bases digitais de licitações públicas do Governo Federal Brasileiro. O objetivo é encontrar indícios de fraudes, tais como conluios e cartéis. Essa tarefa é complexa para os auditores dado que a quantidade de dados disponíveis é muito grande e dada a dificuldade de correlacionar esses dados. Como resultado desse trabalho espera-se que os auditores possam ter um auxílio na tarefa de auditoria de licitações na Controladoria Geral da União (CGU).Data mining has been an area of high visibility in recent years and many researches have shown good efficiency in this area to find information in large databases. This project proposes to use data mining in the digital databases of public bidding of the Brazilian Federal Government. The aim is to find evidence of fraud, such as stunts and cartels. This task is complex for auditors since the amount of data available is very large and given the difficulty of correlating this data. As a result of this work it is expected that the auditors can have an aid in the task of auditing bids in the Controladoria Geral da União (CGU)