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Back to normal?! Data and Technology in Times of Crises

Statistics and contact-tracing applications bear the promise to “master” the COVID-19 crisis — but which systems of power do they actually uphold?

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Graph by https://ourworldindata.org/coronavirus-data; Image by After Katsushika Hokusai — Restored version of File:Great Wave off Kanagawa.jpg, https://commons.wikimedia.org/w/index.php?curid=5576388; Collage inspired by Jonathan Gray @jwyg

Every evening, in this brief moment of silence before going to bed, I pick up my phone and search “Coronavirus” on Google. I know I shouldn’t do it, not on Google, not before going to sleep — but still I really want to check the numbers. Every evening I click on the first website that appears, called worldometers.info. It’s a website owned by a private data company which makes its revenue through online advertisement. It uses cookies and tracks me throughout the web. I am wondering: Where does the need to understand a pandemic through data come from? And, what kind of implications do contact-tracing technologies in times of crises have?

COVID-19 is extremely hard to grasp. It is invisible, yet it is spreading around the world. The virus causes anxiety, uncertainty, death, job loss, and domestic violence. It is multidimensional, and embedded within an ecological disaster. COVID-19 is a hyperobject, or, as Timothy Morton wrote recently, a hyperobject within another hyperobject called climate crisis. It is hard to understand, but at the same time impacts everyone everywhere.

Within this context, data and surveillance technologies promise a means to master this crisis, but they build upon a patriarchal, capitalist and colonial history. To give an example: In 1735, the Swedish botanist Carl Linnaeus published “Systema Naturae”, one of the most comprehensive taxonomies of plants of these times. Linnaeus collected, categorised and organised plants with the ultimate goal to understand and make sense of nature. He longed for systems, species, and classes of plants just like many white male researchers at this time. By aligning leafs, counting flowers and giving them numbers, Linnaeus built a substantial part of the underlying belief that in order to manage nature, one only has to measure it.

By Carl Linnaeus — http://www.botanicus.org/bibliography/b11957918, Public Domain, https://commons.wikimedia.org/w/index.php?curid=4139945

In times of climate crisis, the implications of these assumptions are undeniable. The belief to be able to manage nature by measuring it led to harmful practices of extraction and destruction of resources and caused an ecological disaster which, by neoliberal politicians around the world, is still assumed to be under control. But the promise to master nature is not the only example. The rise of statistics in the late 19th century meant another level of management and control, this time of populations. With the help of “neutral” numbers and graphs, statistics wanted to understand the life of human beings. Particularly in West African colonies that were plagued with cholera, statistics became a means to exert bio-political power over entire populations with the purpose of public health. Political decisions became “informed” decisions, even though statisticians always emphasised probability, not reality.

The worldometers.info website reminds me of this buggy statistics software back at university. The simple layout and the green colour emphasise functionality over design. It’s about the numbers, the categories and the clarity — not the contexts. Figures rise, figures stagnate, everything is ordered and under control. Particularly the columns for each country and the numbers of confirmed cases recall memories of the 2018 world football championships with their lists of goals, points, games per country. COVID-19 appears like a race, a contest, a tournament — the higher the numbers the worse, the more accurate the numbers the better.

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Screenshot worldometers.info

And while analysing these columns, country after country, the discussion in my head remains the same: Yes, the total number of tests in the US is relatively high, but are these tests actually meaningful? Yes, Hungary only shows very little growth of infections, but how do I know that these numbers are true if Orbán has been suppressing freedom of thought for years? Yes, Bangladesh’s numbers of cases are still relatively low, but do only the wealthy have access to these tests?

Clearly, the data lacks stories, experiences — and feelings. The data thinks in deaths, in survivors, in totalities and correlations. It abstracts, and fails to contextualise. But without these contexts, how can it be meaningful at all? “Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care.” (Bowker, 2005, p. 183f). (Here I had to insert the obligatory Bowker citation thanks to danah boyd and Kate Crawford). So even though the graphs on worldometers.info suggest accuracy and precision, the data is raw, and hence has many invisible flaws.

Data promises truth, it claims “reality” and it allows to exert power over others. So it should be no wonder that, in capitalism, there is an entire industry that profits exactly from these promises and claims. The industry is called big tech, and the new solutions it proposes are COVID-19 tracking apps.

Just like big tech ‘fixed’ inefficient book industries (Amazon), inefficient elections (Cambridge Analytica), or inefficient labour markets (TaskRabbit), it now wants to ‘fix’ a pandemic with surveillance technologies.

But the big tech companies not only want to ‘fix’ it on their own accounts, they now want to team up with each other and additionally team up with governments, a mixture that, since the Snowden revelations, should be clear to be extremely dangerous.

It’s exactly what Naomi Klein calls disaster capitalism: businesses and governments take advantage of a crisis by proposing “solutions” which, in fact, broaden social inequalities and exploit people. They talk about the new, the smart, the unthought, the disruptive — but they use invasive applications as a means of control, and they use a disaster for their own revenues. It shouldn’t come as a surprise that exactly in this establishment, CEOs of tech start ups around the world post pictures of themselves on LinkedIn in their homes baking sourdough bread and emphasising the need to see COVID-19 as a chance — not an obstacle.

Meanwhile, the public debate around these tracking apps degrades — again — to an either/or discussion. It’s “privacy” versus “freedom”, it’s “regulation” versus “innovation”, and it’s “health” versus “economy”. The arguers often present only those two options. But it’s important not to fall into the trap of two bad alternatives: there are enough technologies that still ensure privacy, openness and transparency — and we particularly have to demand them now.

Data and technologies are answers to the desire to make the hyperobject COVID-19 understandable and controllable. But data prioritises certain realities over others, it diminishes individual experiences and equalises them through numbers. It abstracts all the suffering and bears the misleading promise to control a virus only through rational thought. Media is filled with graphs, and new applications want to trace infectious diseases in order to be able to go back to “normal”. But what was this “normal” anyway? In times of crises, these technologies are not inherently bad, but they reproduce structures of power that can have a massive impact on our identities and social lives. So it’s worth keeping in mind that both data and technologies have their roots in patriarchy, colonialism, and capitalism. They might promise truth and the “mastering” of a crisis — while actually what they do is trying to uphold a rotten system that is falling apart.

Thank you Siavash Eshghi for editing.

Written by

sustainable tech / feminist futures / data + society / visions of nature + ecologies / co-founder MOTIF Institute / @KatrinFritsch

sustainable tech / feminist futures / data + society / visions of nature + ecologies / co-founder MOTIF Institute / @KatrinFritsch

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