The covid-19 pandemic is a clear example of the need to govern within uncertainty, because decisions cannot be delayed until more knowledge is available on the spread of the virus, its possible developments, long-term health sequels, re-emergence, or the development of a possible vaccine. Yet, the covid-19 is also shaping up to be an example of the politics of certainty. The pursuit of certainty remains the default defense against the uncertainty generated by the pandemic – if not in decision-making, certainly in the business realm, and with consequences for democratic and individual integrity.
The Economist reported on April 17th that for the first time Google and Apple had joined efforts in their information extracting and tracking capacities to improve contact tracing between people. What is remarkable about this joint venture is that the state of emergency has seamlessly normalised an on-going major breach of privacy, as data collection from mobile phones and online apps is not designed to track only location, but is an ever expanding intrusion into all aspects of people’s daily lives, which includes scanning the content of emails and messages, capturing conversations held within mobile phones’ hearing distance, accessing photos, videos being watch, the music one listens to, etc. Among the many data gathered by companies like Google and Apple, is location. Tracking capacities were not developed for the virus. The virus makes this technology seem useful, or even necessary. In her brilliant book Surveillance Capitalism, Shoshana Zuboff argues that the practices of data extraction “impose a new collective order based on total certainty” (2019). This is an example of complexity: in a highly interconnected world – or what can be called a tightly coupled system – a health crisis sparks a new social order into being.
Certainty assumes a new meaning. It is not about the robustness of (scientific) information, its quality or its reliability. Certainty is about the pervasiveness of surveillance, the blurring of boundaries between the private sphere and public interest, and between technocratic modes of governance and deliberative democracy in which uncertainty is dealt with collectively – for as faulty as collective processes may be. Technocratic governance, instead, relies on technical instruments of governance, such as indicators, statistics, storing information and, crucially, processing ever increasing amounts of information: Big Data. The unmanageable amount of data that needs processing, makes understanding a matter of computer simulations and modelling, takes it out of the hands of elected representatives of the public and creates a dependence on technical experts and software. Understanding becomes a post-human prerogative.
In this situation, I argue that once again different understandings of uncertainty matter. If uncertainty is reduced to lack of data, to a temporary problem or to a modelling challenge, then the solution is to collect more and more information, improve modelling capacity and seek answers to the difficult political choices that have to be made in the context of a pandemic in technical expertise. If we understand uncertainty as an epistemic condition, as facing the irreducible limits of scientific knowledge and acknowledging the unpredictability of the world we live in, then the answer may be to defend institutions such as democracy, to value individual integrity, and to learn to live with uncertainty – rather than temporarily coping with it.
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