Machine learning and AI is coming for corrupt officials - Disrupting corruption with a data-driven approach is possible
South Africa has a big problem with corruption in government supply chains. The most salient recent example would be the looting of funds during the Covid-19 pandemic, specifically the procurement of personal protective equipment in the Gauteng health department. Mark Heywood correctlythat unless we introduce the certainty of punishment for corrupt public officials, we will lose the fight against corruption .
Ponder for a moment the transformative power to predict the likelihood of xenophobic attacks or unrest such as the now-infamousWhat if an entire government supply chain can be managed by a distributed ledger like a blockchain, so that not a single public official is involved? This is made possible by two conspiring realities: the costs of storing data has decreased over the years, and computational power has increased. This means that it is possible to find patterns and correlations in very large datasets .
As a social researcher and budding code writer , this makes me wonder about the potential to use an artificial neural network or a decision tree regressor as a catalytic mechanism in the fight against corruption. In the web-native world of data science, anybody can write code, train a computer model and set it free on real data. This is indeed encouraged by the fact that free and open-source resources are now ubiquitous.
The second question is whether the data exists. Let me explain. Data about government performance is everywhere, and it is abundant. In my case Scopa members are inundated with data all the time. Data sources include portfolio committee reports, the auditor general’s office, the Public Service Commission, the Financial and Fiscal Commission, the Special Investigating Unit, internal audit reports, departmental quarterly reports, and the list goes on.