Technology and Integrity Can Save Lives
In 1970, Chile was to conduct the first documented experiment in Latin America to apply centralized computer techniques in government decision-making, based on decentralized data capture. Cybersyn or Project Synco was an initiative led by then-President Salvador Allende, developed by British consultant Stafford Beer.
Cybersyn required a central computer connected to telex machines at the factories, which would input data on the production process, which would then be centrally analyzed. Information was consolidated in a hexagonal operations room, with a ten-meter diameter, with seven swivel chairs and screens on the walls. Analysts could not use desks or paper.
The project was to collect real-time data, design statistical programs, build computerized simulations of Chile’s economy, and communicate with factories by identifying performance-impacting issues, applying an early warning system.
Augusto Pinochet’s coup in 1973 put an end to Cybersyn. Although the tool was intended for centralized production planning to replace market institutions (something that clearly did not work in Chile at the time or in any other country afterwards), its technology paved the way for what we now know as data intelligence.
50 years later, the idea behind Cybersyn cannot be more relevant to governments facing the COVID-19 pandemic today, as they need a mechanism that takes real-time information from hospitals, healthcare centers, and human settlements to identify supply needs and deploy emergency healthcare. Unlike in the time of Stafford Beer and Allende, today data and the technology to process it have been developed and democratized to the point that there are dozens of COVID-19 monitoring initiatives worldwide according to the Open Government Partnership. These initiatives enable citizens and governments to coordinate actions to curb the expansion and mortality rate of the pandemic, under different collaborative models based on consultation and consolidation of open data.
However, the lethality of COVID-19 can be exacerbated by variables that transcend hygiene habits, the effectiveness of lockdown measures, the biological characteristics of coronavirus or the coordination of collective actions based on data; the materialization of corruption risks in emergency healthcare schemes can seriously impinge on the efforts governments and citizens are making to contain the pandemic. Corruption would involve not only excess cost or diverting of funds for emergency care, but deny millions of people goods and services that are literally of life and death. Fortunately, the data-driven and computer technologies Chile intended to implement half a century ago could well save the lives of millions of people around the world and especially in Latin America, where corruption is an obvious institutional problem (CAF, 2019).
The risk of corruption in public procurement schemes to address the emergency is of particular relevance, since the supply chain of goods urgently required by the healthcare sector depends on the success of COVID-19 containment. In that sense CAF identified solutions that use data-driven technologies to give more visibility and transparency to emergency public procurement processes: from real-time, results-based expense allocation and tracking mechanisms, algorithms that automatically send registered suppliers the emergency calls for proposals in the context of the pandemic.
Digital solutions for procurement and sourcing are not the only tools available to citizens and governments to promote integrity in the current emergency. Opening procurement data and the computer technologies to use them can also be harnessed to detect real-time corruption risks. Recently, in Colombia, the Comptroller General warned about the exaggerated cost overruns raising suspicions of corruption in more than 5,000 contracts recently signed by government agencies in the context of the COVID-19 crisis. The Comptroller General can detect anomalies in such quantities and in such a short time thanks to the OCEANO platform, which runs automated data analysis models produced by the Electronic Government Procurement System (SECOP). This type of mechanisms, which rely on the centralization of computer data analysis techniques to determine corruption risks, are increasingly available to governments and citizens. CAF took stock of several initiatives that use data analytics and machine learning to alert authorities about corruption risks, thus changing the corruption-fighting scheme, as it moves from a reactive role based on complaints to a proactive one based on prevention and early detection of possible crimes.
COVID-19 is showing us that digitalization is not a luxury, but an indispensable tool to protect the health of states and markets. Digital technologies applied to integrity policies could very well save everyone’s life.
