Indeed, this type of very complex algorithmic approach creates a significant risk of intellectual debt.
Intellectual debt as a fast-growing governance risk However, perhaps more interestingly, the difficulty in understanding the model creates a significant additional governance risk in itself: intellectual debt. In itself, this lack of explainability would be a major obstacle to the deployment of the solution ‘in the real world’. I think most people with an interest in anti-corruption in procurement would also struggle to understand it and, even data scientists (and even the author of the paper) would be unable to fully understand the reasons why any given contract award is flagged as potentially corrupt by the model, or to provide an adequate explanation. The paper is technically very complex and I have to admit to not entirely understanding the specific workings of the graphical analysis algorithms. In this post, I unpack what is behind that magic and critically assess whether it follows a sound logic on the workings of corruption (which it really doesn’t). Such an approach could be seen to provide a magical* solution to the very complex issue of corruption monitoring in procurement (or more generally). The approach in the paper would create ‘risk maps’ to eg prioritise the investigation of suspected corrupt awards. The paper implements complex algorithms to support graphical analysis to cluster public contracts with the aim of identifying those at risk of corruption. The paper aims to detect corrupt practices by exploiting network relationships among participants in public contracts.
I have read a new working paper on the use of ‘blackbox algorithms’ as anti-corruption screens for public procurement: I Pastor Sanz, ‘ A New Approach to Detecting Irregular Behavior in the Network Structure of Public Contracts’.
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