Abstract
Countries have been develo** 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.
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Introduction
Alice, Agata, Monica, Esmeralda, Iris, Rosie and Rui are, despite their names, non-humans. They were designed by humans to execute tasks previously reserved for humans; they can cross-check data faster and visualise and communicate them better, hel** to identify or even predict anomalies related to different types of corruption in Brazil. They all are bots developed both bottom-up and top-down to work as anti-corruption tools to identify suspicious activities related to, for example, bid-rigging, fraud in contracts, cartel practices, the misuse of public money by congressional representatives, and sluggishness in the Supreme Court. These anti-corruption bots dig into data to increase accountability. By map** these initiatives that have different levels of automation and functionalities, this paper aims to explore the benefits and limitations of using AI technology to fight corruption and improve integrity. In doing so, it questions what jobs these bots have been performing in the fight against corruption.
Like many tech buzzwords, bots and other applications of intelligent systems create great expectations for solving complex issues but attract a high level of distrust and generate debate. This is particularly the case with AI, still a vague concept that does not delineate specific technological advances and deals with subjective tasks classified as intelligent (Lanier & Weyl, 2020). Although no definition of AI has been agreed upon (Nilsson, 2009), the existing attempts have been criticised for being too anthropocentric since other forms of intelligence exist than human-specific (Koos, 2018; Wang, 2019). Yet AI is doubtlessly expected to extend the human and non-human limits of current performance in data processing and analysis, including being used as a powerful anti-corruption tool (Adam & Fazekas, 2021; Aarvik, 2019; Wirtz & Müller, 2019; Köbis et al., 2021). While scholars have explored the use of AI-ACT, mainly in audit activities (Ghedini Ralha & Sarmento Silva, 2012; Neves et al., 2019; Taurion, 2016), Köbis et al. (2022) investigated existing challenges regarding data, algorithms and human–machine interactions and discussed the risks of using this type of technology in reinforcing existing power structures.
In addition, some studies apply AI techniques to predict and explain corruption across countries (Lima & Delen, 2020) or in public contracts (López-Iturriaga & Sanz, 2018), to detect fraud, corruption, and collusion in international development contracts (Grace et al., 2016), and to identify self-reported experiences with corruption on Twitter by using unsupervised machine learning (Li et al., 2020). In turn, the development and use of AI-ACT regarding bottom-up initiatives remain scarce. ProZorro in Ukraine (Aarvik, 2019) and Rosie, the Brazilian bot created by Operação Serenata de Amor (Köbis et al., 2022; Mattoni, 2020; Savaget et al., 2019), are among the few civil society initiatives that have received academic attention. Even fewer studies have looked at the outcomes of emerging technologies from the bottom up. Freire et al. (2020) are among the few who have not only questioned the impact of bottom-up monitoring on public service performance but also provided evidence of the null effect of a crowdsourcing mobile phone application and a Twitter bot (Tá de PéFootnote 1), both developed by the NGO Transparência Brasil, to allow oversight of school construction projects in Brazilian municipalities.
Aiming to contribute to this growing literature, this article provides an empirical analysis of bottom-up and top-down AI-ACT initiatives using Brazil as a case study. The theory combines the frameworks provided by Köbis et al. (2008; Bertot et al., 2010; Davies & Fumega, 2014; Mattoni, 2020; Adam & Fazekas, 2021; Köbis et al., 2022). Sanchez-Graells (2021) is one of the few voices to stress that AI-ACT, at least at its current narrow stage, can promote incremental improvements but not the expected significant transformation in the fight against corruption in public procurement.
Attention has been devoted to understanding under which contexts technological interventions, including AI, are more or less likely to work (Adam & Fazekas, 2021; Freire et al., 2020), which types of constraints and resistance they may encounter among users, such as auditors and inspectors (Neves et al., 2019), and when information and communication technology (ICT) tools have a high or low impact (Peixoto & Fox, 2016) or can be used to engage in corruption (Adam & Fazekas, 2021). However, different from more traditional ICTs, AI-ACT remains to be explored in depth, starting from its conceptualisation, main functionalities, and ethical considerations based on empirical evidence.
When more broadly reviewing the literature on AI models applied to public management, Wirtz and Müller (2019) presented an AI framework identifying three layers: AI technology infrastructure, AI functionality, and AI applications and services. To them, the cornerstone is the technology infrastructure because it determines how data is acquired, processed, and embedded into the greater system of AI-controlled applications (Wirtz & Müller, 2019:1078). Their AI functional layer considers the connection and cooperation of specific parts of hardware and software, and the applications and service layer deals with the interconnection and interrelation of AI techniques converted into toolboxes and/or devices. In turn, Köbis et al. (1 illustrates the layers and the elements in both bottom-up and top-down approaches, defined here as the key components of an AI-ACT, along with examples of their applications, to better understand what job these technologies have been performing in combating corruption.
Data availability
The datasets supporting findings are included in this published article and its supplementary information file. Part of the data not publicly available due to the fact that they constitute an excerpt of research in progress could be made available upon reasonable request and with permission of the ERC funded BIT-ACT (Bottom Up Initiatives and Anti-Corruption Technologies) project.
Notes
Tá de Pé is an informal Brazilian expression for ‘Is it done?’ Literally, it means ‘standing on its feet’ in Portuguese (Freire, Galdino and Mignozzetti, 2020).
The digital transformation of public procurement, court proceedings, and public records, along with governmental open data and transparency policies and additional new data sources generated by satellites, electronic devices, and social media, have proved to be essential in develo** many AI-ACTs currently in place. However, this type of data is not always accessible everywhere with the same quality.
This study acknowledges, however, that improving such types of control can indirectly impact possible attempts to bribe inspectors who control mine licensing and any illegal trade.
MARA was developed by the Brazilian Office of the Comptroller General to estimate the risks of professional misconduct, including corrupt behaviour, among its civil servants by using dozens of variables, including how the person was employed (formal exam or political appointment), political affiliation and criminal background. It trains the algorithm based on a dataset containing details of thousands of workers punished in the past for administrative misconduct (Aarvik 2019; Köbis, Starke, and Rahwan 2022).
See https://catalogoia.omeka.net/. Accessed: 22 April 2021.
See Achados and Perdidos from Transparência Brasi.
l (http://www.achadosepedidos.org.br/usuarios/tburg) and the governmental website Busca de Pedidos e Respostas (http://www.consultaesic.cgu.gov.br/busca/SitePages/principal.aspx).
‘Delphos’ Catálogo de Uso de Inteligência Artificial por Órgãos Governamentais. Accessed on April 22, 2021, https://catalogoia.omeka.net/items/show/34.
Operation Car Wash was an investigation into money laundering that became public in March 2014 and quickly turned into a much greater corruption probe, uncovering a wide and intricate web of political and corporate racketeering with unprecedented repercussions not only in Brazil but in several other Latin American and African countries (Lagunes et al., 2021).
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Acknowledgements
The author acknowledge that the research, authorship and publication of this article was conducted in the framework of the BIT-ACT project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No 802362).
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Open access funding provided by Alma Mater Studiorum - Università di Bologna within the CRUI-CARE Agreement.
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Previous versions of this manuscript were presented at the ECPR Joint Session 2021 “Digital Media, Machine Learning, and Corruption: How the Newest Technological Development Facilitate and Curb Corruption Practices Across the World” organized by prof Alice Mattoni and Dr Roxana Bratu (May 2021) and at the International Seminar Artificial Intelligence: Democracy and Social Impacts (December 2021) organized by the C4AI/USP.
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Odilla, F. Bots against corruption: Exploring the benefits and limitations of AI-based anti-corruption technology. Crime Law Soc Change 80, 353–396 (2023). https://doi.org/10.1007/s10611-023-10091-0
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DOI: https://doi.org/10.1007/s10611-023-10091-0