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.

Research that examines the impact of economic, social, and political factors on political corruption uses expert’ and citizen’ perceptions for measuring corruption and testing arguments. Scholars argue that the perception of corruption is a good proxy for actual corruption because data on actual corruption are limited and not entirely trustworthy. However, perception indexes do not allow for testing separate mechanisms driving citizen’ perceptions of corruption from actual levels of corruption in different government branches. To address this issue, I introduce a new index based on Latin American countries to measure the risk of corruption in political parties. Using a de jure analysis of laws and regulations, the Risk of Corruption (ROC) index evaluates the likelihood of political parties engaging in corrupt activities. Instead of measuring corrupt activities or perception directly, the ROC measures the risks of involving in corruption. The index has important implications for academics and practitioners in anti-corruption issues. First, it allows us to test arguments about the role of political parties and legislatures in reducing political corruption. Second, it helps to understand how political parties could improve their internal organization to decrease the risk of corrupt activities. Finally, it is a valuable instrument for cross-national studies in diverse fields that study political parties.

Digital identity systems are promoted with the promise of great benefit and inclusion. The case of the Ugandan digital identity system demonstrates that the impact of digital identity systems is not only positive but also has negative impacts, significantly affecting human lives for the worse. The impact on the human lives of digital identity systems can be assessed by multiple frameworks. A specific framework that has been mentioned is the capabilities approach (CA). This article demonstrates that the CA is a framework to assess the impact on human lives that can be operationalized for technology and information and communication technology, including digital identity systems. Further research is required to compare the CA with other candidate evaluation frameworks.

We present PolicyCLOUD: a prototype for an extensible serverless cloud-based system that supports evidence-based elaboration and analysis of policies. PolicyCLOUD allows flexible exploitation and management of policy-relevant dataflows, by enabling the practitioner to register datasets and specify a sequence of transformations and/or information extraction through registered ingest functions. Once a possibly transformed dataset has been ingested, additional insights can be retrieved by further applying registered analytic functions to it. PolicyCLOUD was built as an extensible framework toward the creation of an analytic ecosystem. As of now, we have developed several essential ingest and analytic functions that are built-in within the framework. They include data cleaning, enhanced interoperability, and sentiment analysis generic functions; in addition, a trend analysis function is being created as a new built-in function. PolicyCLOUD has also the ability to tap on the analytic capabilities of external tools; we demonstrate this with a social dynamics tool implemented in conjunction with PolicyCLOUD, and describe how this stand-alone tool can be integrated with the PolicyCLOUD platform to enrich it with policy modeling, design and simulation capabilities. Furthermore, PolicyCLOUD is supported by a tailor-made legal and ethical framework derived from privacy/data protection best practices and existing standards at the EU level, which regulates the usage and dissemination of datasets and analytic functions throughout its policy-relevant dataflows. The article describes and evaluates the application of PolicyCLOUD to four families of pilots that cover a wide range of policy scenarios.

Fiduciary agents and trust-based institutions are increasingly proposed and considered in legal, regulatory, and ethical discourse as an alternative or addition to a control-based model of data management. Instead of leaving it up to the citizen to decide what to do with her data and to ensure that her best interests are met, an independent person or organization will act on her behalf, potentially also taking into account the general interest. By ensuring that these interests are protected, the hope is that citizens’ willingness to share data will increase, thereby allowing for more data-driven projects. Thus, trust-based models are presented as a win–win scenario. It is clear, however, that there are also apparent dangers entailed with trust-based approaches. Especially one model, that of data trusts, may have far-reaching consequences.