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Artificial Intelligence – a promising anti-corruption tool in development settings?

There are relatively few examples of how artificial intelligence has been deployed in anti-corruption work. Such tools are normally deployed by financial institutions or tax-authorities to uncover money-laundering, fraud, or tax evasion. This study points to two different strategies for using artificial intelligence to aid anti-corruption efforts. It can be applied to uncover corruption that was previously difficult to detect. Secondly, it is possible to design novel artificial intelligence-assisted processes with the aim to avoid previously corruption-prone procedures.

13 June 2019
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Main points

  • The ability of AI applications to work with datasets too large for manual handling make it possible to reveal or even predict corruption or fraud that previously was nearly or completely impossible to detect.
  • AI-assisted procedures can replace previously corruption-prone processes.
  • Digitisation is a prerequisite for AI to be deployed in anti-corruption efforts.
  • Only a handful of countries in Africa have the level of digitisation in society to take advantage of AI.
  • Mobile call data or social media data are in some cases also considered to be possible data sources for anti-corruption applications.
  • Privacy concerns, surveillance issues, and possibly opaque decision-making processes are ethical challenges involved in the development of AI-driven systems.

Cite this publication

Aarvik, P.; (2019) Artificial Intelligence – a promising anti-corruption tool in development settings?. Bergen: U4 Anti-Corruption Resource Centre, Chr. Michelsen Institute (U4 Report 2019:1)

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About the author

Per is an independent writer on applied digital technology for humanitarianism, development, governance and anti-corruption. Social media data, satellite imagery, geographical information systems, and applied artificial intelligence are among his interests. He holds a Master's degree in Democracy Building from the Department of Comparative Politics, University of Bergen, Norway. His thesis focused on the potential of crowdsourced civil society election monitoring as a tool to combat election fraud. His background is from journalism, advertising and higher design education – as a practitioner, educator, and in managerial roles. In recent years he has led digital humanitarian work during disasters and in democracy projects.


All views in this text are the author(s)’, and may differ from the U4 partner agencies’ policies.

This work is licenced under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0)


artificial intelligence, anti-corruption measures, e-government, ethics, fraud detection, governance, public procurement, Kenya, India, Brazil