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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.
Fernanda Odilla (Sat,) studied this question.
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