TY - GEN
T1 - Weak Supervision Can Help Detecting Corruption in Public Procurement
AU - Tas, Bedri Kamil Onur
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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PY - 2023
Y1 - 2023
N2 - Corruption in public procurement in the European Union is estimated to cost more than 120 billion Euros per year. Detecting corruption in public procurement is challenging because of imperfect corruption reg flags and vast number of tenders. Authorities need potent and scalable tools to identify corrupt public procurement practices. This paper shows that weak supervision machine learning algorithm provides a proficient method that can easily be implemented. Authorities can effectively calculate corruption probabilities of millions of public procurement tenders using the weak supervision algorithm. Weak supervision combines information contents of imperfect corruption red flags with unknown accuracies. Additionally, it can handle measurement errors. I analyze potential corruption in 25,859,734 European Union (EU) public procurement contracts in years 2009–2020 using the Snorkel weak supervision algorithm. These contracts are awarded by 1,212,533 authorities in 33 EU and affiliated countries. The analysis suggests that 40% of contracts and 22% of EU authorities are susceptible to corruption. Experimental results show that training machine learning models with weak supervision labeled data produces superior prediction results.
AB - Corruption in public procurement in the European Union is estimated to cost more than 120 billion Euros per year. Detecting corruption in public procurement is challenging because of imperfect corruption reg flags and vast number of tenders. Authorities need potent and scalable tools to identify corrupt public procurement practices. This paper shows that weak supervision machine learning algorithm provides a proficient method that can easily be implemented. Authorities can effectively calculate corruption probabilities of millions of public procurement tenders using the weak supervision algorithm. Weak supervision combines information contents of imperfect corruption red flags with unknown accuracies. Additionally, it can handle measurement errors. I analyze potential corruption in 25,859,734 European Union (EU) public procurement contracts in years 2009–2020 using the Snorkel weak supervision algorithm. These contracts are awarded by 1,212,533 authorities in 33 EU and affiliated countries. The analysis suggests that 40% of contracts and 22% of EU authorities are susceptible to corruption. Experimental results show that training machine learning models with weak supervision labeled data produces superior prediction results.
KW - Corruption
KW - Public procurement
KW - Weak supervision
UR - http://www.scopus.com/inward/record.url?scp=85138266248&partnerID=8YFLogxK
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UR - https://www.mendeley.com/catalogue/c0dbae46-cf43-3a38-b290-5997998e0dd8/
U2 - 10.1007/978-3-031-16075-2_40
DO - 10.1007/978-3-031-16075-2_40
M3 - Conference contribution
AN - SCOPUS:85138266248
SN - 9783031160745
T3 - Lecture Notes in Networks and Systems
SP - 548
EP - 555
BT - Intelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 3
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2022
Y2 - 1 September 2022 through 2 September 2022
ER -