Predicting and explaining corruption across countries: A machine learning approach

Bots against corruption: Exploring the benefits and limitations of AI-based anti-corruption technology

Countries have been developing and deploying anti-corruption tools based on artificial intelligence with hopes of them having positive capabilities. Yet, we still lack empirical analyses of these automated systems designed to identify and curb corruption. Hence, this article explores novel data on 31 bottom-up and top-down initiatives in Brazil, presented as a case study. Methodologically, it uses a qualitative analysis and draws on secondary data and interviews to assess the most common features, usages and constraints of these tools. Data collected are scrutinised under a new conceptual framework that considers how these tools operate, who created them for what purpose, who uses and monitors these tools, what types of corruption they are targeting, and what their tangible outcomes are. Findings suggest that in Brazil, AI-based anti-corruption technology has been tailored by tech-savvy civil servants working for law enforcement agencies and by concerned citizens with tech skills to take over the key tasks of mining and crosschecking large datasets, aiming to monitor, identify, report and predict risks and flag suspicions related to clear-cut unlawful cases. The target is corruption in key governmental functions, mainly public spending. While most of the governmental tools still lack transparency, bottom-up initiatives struggle to expand their scope due to high dependence on and limited access to open data. Because this new technology is seen as supporting human action, a low level of concern related to biased codes has been observed.

Machine learning applied to healthcare: a conceptual review

The technological inference in procedures applied to healthcare is frequently investigated in order to understand the real contribution to decision-making and clinical improvement. In this context, the theoretical field of machine learning has suitably presented itself. The objective of this research is to identify the main machine learning algorithms used in healthcare through the methodology of a systematic literature review. Considering the time frame of the last twenty years, 173 studies were mined based on established criteria, which allowed the grouping of algorithms into typologies. Supervised Learning, Unsupervised Learning, and Deep Learning were the groups derived from the studies mined, establishing 59 works employed. We expect that this research will stimulate investigations towards machine learning applications in healthcare.

Predicting politicians’ misconduct: Evidence from Colombia

Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine-learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the receiver-operating characteristic curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important in predicting corruption.

ARTIFICIAL INTELLIGENCE ADOPTION FOR E-GOVERNMENT - ANALYSIS OF ENABLERS IN AN EMERGING ECONOMY

Recent advancements in Artificial Intelligence (AI), is expected to have a major impact on the ways governments provide services to the population. The major objective of this paper is to identify success factors for AI adoption in public sector organizations and understand the interrelationships among them. Eleven factors were identified from the literature and were modeled using ISM method. The results show that “Top Management Support” and “Supportive Regulatory Environment” as most important drivers enabling AI adoption in e-government. Further, the variable “Training & Skill Development” is found to be a critical link in developing “AI driven Services Ecosystem” leading to business process transformation in e-government. The findings of this study are expected to provide an insight to further improve and promote adoption of AI based solution in facilitating e-government. The results of the study are among the first academic attempt to shed light on the key role of enablers supporting AI adoption in an emerging economy.

Artificial Intelligence-Driven Digital Technologies to the Implementation of the Sustainable Development Goals: A Perspective from Brazil and Portugal

Innovativeness is a characteristic of digital technologies (DT), and they have been assuming an important role in economic, social, and environmental dimensions. Therefore, DT are relevant contributors for sustainable development goal (SDG) achievements. This study aims to compare the preference for artificial intelligence-driven digital technologies (AI-Driven DT) to achieve SDGs in Brazil and Portugal. An independent sample t-test analysis and Levene test are performed to identify potential artificial intelligence-driven digital technologies (AI-Driven DT) as favorable facilitators for SDG achievements in Brazil and Portugal. Based on the findings, a broader analysis is provided, to (i) indicate potential favorable SDGs, (ii) discuss differences between the countries in AI-Driven DT preferences in each SDG, and (iii) make recommendations for potential technologies that could receive more attention and investments in both regions to make emergent digital technologies succeed, with a particular emphasis on cleaner production. The analysis is organized into three dimensions: economic, social, and environment. At the end, a closing discussion is provided about the key guidelines and prospects that could be adopted to keep a strong and positive shift of AI-Driven DT developments and applications towards fully supporting the attainment of the SDG of United Nations Organization (ONU) Agenda 2030.

Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.

Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means

The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.

Corruption and instutitions: An analysis for the Colombian case

This paper identifies the main determinants of errors in the allocation of spending by the Colombian Government. Using information from the Electronic Public Procurement System (SECOP), the determinants of the probability of an addition to a contract are identified. The errors of the government can be interpreted as an approximation of their corruption. The average income and educational level of a colombian department are found to directly influence the probability of an addition. Using the estimation of the binary choice models, the forecast error of an addition is estimated, it is found that public and civil works contracts have more forecast error, forming an ideal mechanism for thefts and accumulation of bribes. Our results show that predicting an addition can be done with high certainty.