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The promise and perils of using artificial intelligence to fight corruption

Abstract

Corruption presents one of the biggest challenges of our time, and much hope is placed in artificial intelligence (AI) to combat it. Although the growing number of AI-based anti-corruption tools (AI-ACT) have been summarized, a critical examination of their promises and perils is lacking. Here we argue that the success of AI-ACT strongly depends on whether they are implemented top–down (by governments) or bottom–up (by citizens, non-governmental organizations or journalists). Top–down use of AI-ACT can consolidate power structures and thereby pose new corruption risks. Bottom–up use of AI-ACT has the potential to provide unprecedented means for the citizenry to keep their government and bureaucratic officials in check. We outline the societal and technical challenges that need to be overcome to harness the potential for AI to fight corruption.

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Fig. 1: Schematic overview of top–down and bottom–up anti-corruption efforts.

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References

  1. Rothstein, B. & Varraich, A. Making Sense of Corruption (Cambridge Univ. Press, 2017).

  2. Köbis, N. C., van Prooijen, J.-W., Righetti, F. & Van Lange, P. A. M. The road to bribery and corruption: slippery slope or steep cliff? Psychol. Sci. 28, 297–306 (2017).

    Article  Google Scholar 

  3. Fisman, R. & Golden, M. A. Corruption: What Everyone Needs to Know (Oxford Univ. Press, 2017).

  4. Rothstein, B. The Quality of Government: Corruption, Social Trust and Inequality in International Perspective (Univ. Chicago Press, 2011).

  5. Mungiu-Pippidi, A. & Heywood, P. A Research Agenda for Studies of Corruption (Edward Elgar, 2020).

  6. Mungiu-Pippidi, A. The time has come for evidence-based anticorruption. Nat. Hum. Behav. 1, 0011 (2017).

    Article  Google Scholar 

  7. Fisman, R. & Golden, M. How to fight corruption. Science 356, 803–804 (2017).

    Article  Google Scholar 

  8. High-Level Expert Group on Artificial Intelligence. A Definition of AI: Main Capabilities and Disciplines; https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341 (European Commission, 2019).

  9. Petheram, A. & Asati, I. N. From Open Data to Artificial Intelligence: The Next Frontier in Anti-corruption; https://www.oxfordinsights.com/insights/aiforanticorruption (Oxford Insights, 2018).

  10. Aarvik, P. Artificial Intelligence a Promising Anticorruption Tool in Development Settings (Chr. Michelsen Institute, 2019).

  11. World Bank. Artificial Intelligence in the Public Sector: Maximizing Opportunities, Managing Risks (World Bank, 2020).

  12. Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019).

    Article  Google Scholar 

  13. Adam, I. & Fazekas, M. Are Emerging Technologies Helping Win the Fight Against Corruption in Developing Countries? Pathways for Prosperity Commission Background Paper Series No. 21 (Pathways for Prosperity Commission, 2018).

  14. Lavigne, S., Clifton, B. & Tseng, F. Predicting financial crime: augmenting the predictive policing arsenal. Preprint at https://arxiv.org/abs/1704.07826 (2017).

  15. López-Iturriaga, F. J. & Sanz, I. P. Predicting public corruption with neural networks: an analysis of Spanish provinces. Soc. Indic. Res. 140, 975–998 (2018).

    Article  Google Scholar 

  16. De Blasio, G. et al. Predicting corruption crimes with machine learning. A study for the Italian municipalities. Preprint at http://www.diss.uniroma1.it/sites/default/files/allegati/DiSSE_deBlasioetal_wp16_2020.pdf (2020).

  17. Obermaier, F. & Obermayer, B. The Panama Papers: Breaking the Story of How the Rich and Powerful Hide Their Money (Simon & Schuster, 2017).

  18. King, T. C., Aggarwal, N., Taddeo, M. & Floridi, L. Artificial Intelligence crime: an interdisciplinary analysis of foreseeable threats and solutions. Sci. Eng. Ethics 26, 89–120 (2020).

    Article  Google Scholar 

  19. Wrede, A. Facing Future Corruption Challenges—Trends of the Next Decade; https://www.transparency.org/en/blog/facing-future-corruption-challenges-trends-of-the-next-decade (Transparency International 2019).

  20. Kossow, N. in A Research Agenda for Studies of Corruption (eds Mungiu-Pippidi, A. & Heywood, P.) 146–157 (Edward Elgar, 2020).

  21. Anti-Corruption Technology Solutions—About; https://www.microsoft.com/en-us/microsoftacts/about (Microsoft).

  22. Leon, S. How Can We Use Artificial Intelligence to Help Us Fight Corruption in the Mining Sector?; https://www.globalwitness.org/en/blog/how-can-we-use-artificial-intelligence-help-us-fight-corruption-mining-sector/ (Global Witness, 2018).

  23. Leib, M., Köbis, N. C., Soraperra, I., Weisel, O. & Shalvi, S. Collaborative dishonesty: a meta-study. Psychol. Bull. 147, 1241–1268 (2021).

    Article  Google Scholar 

  24. Köbis, N. C., Verschuere, B., Bereby-Meyer, Y., Rand, D. & Shalvi, S. Intuitive honesty versus dishonesty: meta-analytic evidence. Perspect. Psychol. Sci. 14, 778–796 (2019).

    Article  Google Scholar 

  25. Giubilini, A. & Savulescu, J. The artificial moral advisor. The ‘ideal observer’ meets. Artif. Intell. Philos. Technol. 31, 169–188 (2018).

    Google Scholar 

  26. Kahneman, D., Sibony, O. & Sunstein, C. R. Noise: A Flaw in Human Judgment (Hachette, 2021).

  27. Stephenson, M. Corruption as a self-reinforcing trap: implications for reform strategy. World Bank Res. Obs. 35, 192–226 (2020).

    Article  Google Scholar 

  28. Rahwan, I. Society-in-the-loop: programming the algorithmic social contract. Ethics Inf. Technol. 20, 5–14 (2018).

    Article  Google Scholar 

  29. Kerr, A., Barry, M. & Kelleher, J. D. Expectations of artificial intelligence and the performativity of ethics: implications for communication governance. Big Data Soc. 7, 2053951720915939 (2020).

    Article  Google Scholar 

  30. Schwickerath, A. K., Varraich, A. & Lee Smith, L. (eds) How to research corruption. In Conference Proceedings Interdisciplinary Corruption Research Forum June 7–8 (Interdisciplinary Corruption Research Network, 2016).

  31. Marzagão, T. Using AI to Fight Corruption in the Brazilian Government; https://files.speakerdeck.com/presentations/9c98a23fd1be410db8b71574a4e852b3/Evidence2Action2017.pdf (Observatory of Public Spending, 2017).

  32. Starke, C., Naab, T. K. & Scherer, H. Free to expose corruption: the impact of media freedom, Internet access and governmental online service delivery on corruption. Int. J. Commun. Syst. 10, 21 (2016).

    Google Scholar 

  33. Harrison, G., Hanson, J., Jacinto, C., Ramirez, J. & Ur, B. An empirical study on the perceived fairness of realistic, imperfect machine learning models. In Proc. 2020 Conference on Fairness, Accountability and Transparency 392–402 (Association for Computing Machinery, 2020).

  34. Kearns, M. & Roth, A. The Ethical Algorithm: The Science of Socially Aware Algorithm Design (Oxford Univ. Press, 2019).

  35. Rauh, C. Validating a sentiment dictionary for German political language—a workbench note. J. Inf. Technol. Politics 15, 319–343 (2018).

    Article  Google Scholar 

  36. Jobin, A., Ienca, M. & Vayena, E. The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1, 389–399 (2019).

    Article  Google Scholar 

  37. Taddeo, M. & Floridi, L. How AI can be a force for good. Science 361, 751–752 (2018).

    Article  MathSciNet  MATH  Google Scholar 

  38. Starke, C. & Luenich, M. Artificial Intelligence for EU decision-making. Effects on citizens perceptions of input, throughput and output legitimacy. Data Policy 2, E16 (2020).

    Article  Google Scholar 

  39. Köbis, N. C., Bonnefon, J.-F. & Rahwan, I. Bad machines corrupt good morals. Nat. Hum. Behav. 5, 679–685 (2021).

    Article  Google Scholar 

  40. Acemoglu, D. Harms of AI. Working Paper 29247; https://doi.org/10.3386/w29247 (NBER, 2021).

  41. Pinker, S., Nowak, M. A. & Lee, J. J. The logic of indirect speech. Proc. Natl Acad. Sci. USA 105, 833–838 (2008).

    Article  Google Scholar 

  42. Russell, B. Power: A New Social Analysis (George Allen & Unwin, 1938).

  43. Kalluri, P. Don’t ask if artificial intelligence is good or fair, ask how it shifts power. Nature 583, 169 (2020).

    Article  Google Scholar 

  44. Crawford, K. Atlas of AI (Yale Univ. Press, 2021).

  45. Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (Profile Books, 2019).

  46. Shortening the Risk Lifecycle (Microsoft); https://www.microsoft.com/en-us/microsoftacts/shortening-the-risk-lifecycle

  47. Chen, S. Is China’s corruption-busting AI system ‘Zero Trust’ being turned off for being too efficient? South China Morning Post; https://www.scmp.com/news/china/science/article/2184857/chinas-corruption-busting-ai-system-zero-trust-being-turned-being (4 February 2019).

  48. Kipnis, D. The Powerholders Vol. 230 (Univ. Chicago Press, 1976).

  49. Bendahan, S., Zehnder, C., Pralong, F. P. & Antonakis, J. Leader corruption depends on power and testosterone. Leadersh. Q. 26, 101–122 (2015).

    Article  Google Scholar 

  50. Keltner, D., Gruenfeld, D. H. & Anderson, C. Power, approach and inhibition. Psychol. Rev. 110, 265–284 (2003).

    Article  Google Scholar 

  51. Bowman, J. S. & West, J. P. Lord Acton and employment doctrines: absolute power and the spread of at-will employment. J. Bus. Ethics 74, 119–130 (2007).

    Article  Google Scholar 

  52. Laskowski, P., Johnson, B., Maillart, T. & Chuang, J. Government surveillance and incentives to abuse power. Preprint at https://www.ischool.berkeley.edu/sites/default/files/government-surveillance-abuse-incentives.pdf (2014).

  53. Jenkins, M. Algorithms in Public Administration: How Do We Ensure They Serve the Common Good, Not Abuses of Power?—Blog (Transparency International, 2021); https://www.transparency.org/en/blog/algorithms-artificial-intelligence-public-administration-transparency-accountability

  54. Mattoni, A. In Sociology and Digital Media (eds Rohlinger, D. A. & Sobieraj, S.) (Oxford Univ. Press, 2021).

  55. Mattoni, A. The grounded theory method to study data-enabled activism against corruption: between global communicative infrastructures and local activists’ experiences of big data. Eur. J. Disord. Commun. 35, 265–277 (2020).

    Article  Google Scholar 

  56. Camaj, L. The media’s role in fighting corruption: media effects on governmental accountability. Int. J. Press Polit. 18, 21–42 (2013).

    Article  Google Scholar 

  57. Köbis, N. C., Iragorri-Carter, D. & Starke, C. in Corruption and Norms (eds Kubbe, I. & Engelbert, A.) 31–52 (Springer, 2018).

  58. Acemoglu, D. & Robinson, J. A. De facto political power and institutional persistence. Am. Econ. Rev. 96, 325–330 (2006).

    Article  Google Scholar 

  59. Persson, A., Rothstein, B. & Teorell, J. Why anticorruption reforms fail-systemic corruption as a collective action problem. Governance 26, 449–471 (2013).

    Article  Google Scholar 

  60. Ryman-Tubb, N. F., Krause, P. & Garn, W. How artificial intelligence and machine learning research impacts payment card fraud detection: a survey and industry benchmark. Eng. Appl. Artif. Intell. 76, 130–157 (2018).

    Article  Google Scholar 

  61. Crawford, K. et al. AI Now 2019 Report (AI Now Institute, 2019).

  62. Crawford, K. Regulate facial-recognition technology. Nature 572, 565–565 (2019).

    Article  Google Scholar 

  63. Oksha, N. Empowering Citizens as Watchdogs in Ukraine; https://www.opengovpartnership.org/stories/lessons-from-reformers-empowering-citizens-as-watchdogs-in-ukraine/ (Open Government Partnership, 2019).

  64. Odilla, F. Bots against corruption: exploring benefits and limitations of AI-based anti-corruption technology. In Proc. International Seminar Artificial Intelligence: Democracy and Social Impacts (USP, 2021).

  65. Attard, J., Orlandi, F., Scerri, S. & Auer, S. A systematic review of open government data initiatives. Gov. Inf. Q. 32, 399–418 (2015).

    Article  Google Scholar 

  66. Mayernik, M. S. Open data: accountability and transparency. Big Data Soc. 4, 2053951717718853 (2017).

    Article  Google Scholar 

  67. Flyverbom, M. & Murray, J. Datastructuring—organizing and curating digital traces into action. Big Data Soc. 5, 2053951718799114 (2018).

    Article  Google Scholar 

  68. Rafaeli, A., Ashtar, S. & Altman, D. Digital traces: new data, resources and tools for psychological-science research. Curr. Dir. Psychol. Sci. 28, 560–566 (2019).

    Article  Google Scholar 

  69. Boeschoten, L., Ausloos, J., Möller, J. E., Araujo, T. & Oberski, D. L. A framework for digital trace data collection through data donation. Preprint at https://arxiv.org/abs/2011.09851v1.

  70. Karakaya, A.-S., Hasenburg, J. & Bermbach, D. SimRa: using crowdsourcing to identify near miss hotspots in bicycle traffic. Pervasive Mob. Comput. 67, 101197 (2020).

    Article  Google Scholar 

  71. Vincent, J. ‘Hey Siri, I’m getting pulled over’ Shortcut Makes it Easy to Record Police; https://www.theverge.com/2020/6/17/21293996/siri-iphone-shortcut-pulled-over-police-starts-recording-video (The Verge, 2020).

  72. della Porta, D. & Mattoni, A. Spreading Protest: Social Movements in Times of Crisis (ECPR Press, 2014).

  73. Schroth, P. W. & Sharma, P. Transnational law and technology as potential forces against corruption in Africa. Manag. Decis. 41, 296–303 (2003).

    Article  Google Scholar 

  74. Murillo, M. J. Evaluating the role of online data availability: the case of economic and institutional transparency in sixteen Latin American nations. Int. Polit. Sci. Rev. 36, 42–59 (2015).

    Article  Google Scholar 

  75. Ananny, M. & Crawford, K. Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc. 20, 973–989 (2018).

    Article  Google Scholar 

  76. Jasanoff, S. Virtual, visible and actionable: data assemblages and the sightlines of justice. Big Data Soc. 4, 2053951717724477 (2017).

    Article  Google Scholar 

  77. Seligsohn, D., Liu, M. & Zhang, B. The sound of one hand clapping: transparency without accountability. Env. Polit. 27, 804–829 (2018).

    Article  Google Scholar 

  78. Mittal, M., Wu, W., Rubin, S., Madden, S. & Hartmann, B. Bribecaster: documenting bribes through community participation. In Proc. ACM 2012 Conference on Computer Supported Cooperative Work Companion 171–174 (Association for Computing Machinery, 2012).

  79. Using an App to Curb Corruption; https://www.giz.de/en/mediacenter/39294.html (giz. Nigeria, 2016).

  80. How Barbie, Comedians and New Tech are Opening up Mexico’s Government; https://apolitical.co/solution-articles/en/mexico-labora-supercivicos-open-data (Apolitical, 2016).

  81. Seering, J., Luria, M., Kaufman, G. & Hammer, J. Beyond dyadic interactions: considering chatbots as community members. In Proc. 2019 CHI Conference on Human Factors in Computing Systems 1–13 (Association for Computing Machinery, 2019).

  82. Grau, P., Naderi, B. & Kim, J. Personalized motivation-supportive messages for increasing participation in crowd-civic systems. In Proc. ACM on Human-Computer Interaction Vol. 2, 1–22 (Association for Computing Machinery, 2018).

  83. Li, J., Chen, W.-H., Xu, Q., Shah, N. & Mackey, T. Leveraging big data to identify corruption as an SDG goal 16 humanitarian technology. In Proc. 2019 IEEE Global Humanitarian Technology Conference (GHTC) 1–4 (IEEE, 2019).

  84. Savage, S., Monroy-Hernandez, A. & Höllerer, T. Botivist: calling volunteers to action using online bots. In Proc. 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing 813–822 (Association for Computing Machinery, 2016).

  85. Cerulus, L. Wikileaks Violated Privacy Rights of Hundreds of People: AP; https://www.politico.eu/article/wikileaks-violated-privacy-rights-of-hundreds-of-people-ap/ (Politico, 2016).

  86. Giles, M. The Cambridge Analytica Affair Reveals Facebook’s ‘Transparency Paradox’ (MIT Technology Review, 2018).

  87. Kossow, N. & Miszta, E. Beyond the Hype: Distributed Ledger Technology in the Field of Public Administration. Working Paper No. 58; https://opus4.kobv.de/opus4-hsog/frontdoor/index/index/docId/3504 (ERCAS, 2019).

  88. Aggarwal, N. & Floridi, L. The opportunities and challenges of blockchain in the fight against government corruption. In 19th General Activity Report (2018) of the Council of Europe Group of States Against Corruption (GRECO) (2018).

  89. Bosri, R., Rahman, M. S., Bhuiyan, M. Z. A. & Al Omar, A. Integrating blockchain with artificial intelligence for privacy-preserving recommender systems. IEEE Trans. Netw. Sci. Eng. 8, 1009–1018 (2021).

    Article  MathSciNet  Google Scholar 

  90. AlShamsi, M., Salloum, S. A., Alshurideh, M. & Abdallah, S. in Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications (eds Hassanien, A. E., Bhatnagar, R. & Darwish, A.) 219–230 (Springer, 2021).

  91. Hyvärinen, H., Risius, M. & Friis, G. A blockchain-based approach towards overcoming financial fraud in public sector services. Bus. Inf. Syst. Eng. 59, 441–456 (2017).

    Article  Google Scholar 

  92. Batubara, F. R., Ubacht, J. & Janssen, M. Challenges of blockchain technology adoption for e-government: a systematic literature review. In Proc. 19th Annual International Conference on Digital Government Research: Governance in the Data Age 1–9 (Association for Computing Machinery, 2018).

  93. Casino, F., Dasaklis, T. K. & Patsakis, C. A systematic literature review of blockchain-based applications: current status, classification and open issues. Telemat. Inform. 36, 55–81 (2019).

    Article  Google Scholar 

  94. Aarvik, P. Blockchain as an Anti-Corruption Tool; https://www.u4.no/publications/are-blockchain-technologies-efficient-in-combatting-corruption (U4, 2020).

  95. Shang, Q. & Price, A. A blockchain-based land titling project in the republic of Georgia: rebuilding public trust and lessons for future pilot projects. Innov. Technol. Gov. Glob. 12, 72–78 (2019).

    Google Scholar 

  96. Abadi, J. & Brunnermeier, M. Blockchain Economics. Working Paper 25407; https://doi.org/10.3386/w25407 (NBER, 2018).

  97. David-Barrett, L. State Capture and Inequality; https://cic.nyu.edu/sites/default/files/cic_pathfinders_state_capture_inequality-2021.pdf (NYU Center on International Cooperation, 2021).

  98. AI Procurement in a Box; https://www.weforum.org/reports/ai-procurement-in-a-box (World Economic Forum, 2020).

  99. Calvaresi, D., Mualla, Y., Najjar, A., Galland, S. & Schumacher, M. in Explainable, Transparent Autonomous Agents and Multi-Agent Systems 41–58 (Springer, 2019).

  100. Tufekci, Z. & Wilson, C. Social media and the decision to participate in political protest: observations from Tahrir Square. J. Commun. 62, 363–379 (2012).

    Article  Google Scholar 

  101. Lange, D. A. Multidimensional conceptualization of organizational corruption control. AMRO 33, 710–729 (2008).

    Article  Google Scholar 

  102. Heidenheimer, A. J. & Johnston, M. Political Corruption: Concepts and Contexts (Transaction Publishers, 2011).

  103. Heywood, P. Rethinking corruption: hocus-pocus, locus and focus. Slav. East Eur. Rev. 95, 21–48 (2017).

    Article  Google Scholar 

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N.K. provide conceptualization and visualization, wrote the original draft and reviewed and edited the manuscript. C.S. provided conceptualization, wrote the original draft and reviewed and edited the manuscript. I.R. reviewed and edited the manuscript and provided supervision and visualization.

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Correspondence to Nils Köbis.

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Köbis, N., Starke, C. & Rahwan, I. The promise and perils of using artificial intelligence to fight corruption. Nat Mach Intell 4, 418–424 (2022). https://doi.org/10.1038/s42256-022-00489-1

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