In a recent opinion piece for Al Jazeera, Brandee Butler argues that European Union countries need to collect more equality data to address systemic racism in the region. The argument is framed in the context of the toll COVID-19 has taken on racialised groups, but makes a more general argument: if we had “equality data based on race and ethnic origin, using methodologies guided by minority groups”, Europe could more “meaningfully address systemic racism to forge a more inclusive and enduring Union”.
Butler, the director of the Civil Liberties Division at the Open Society Foundations’ Initiative for Europe, is right to argue that COVID-19 recovery measures should address the needs of racialised and marginalised communities. Yet, her conclusion that Europe’s ability to meaningfully address racial inequality is contingent upon collecting more data lends itself to a data-solutionist approach to justice that must be supplemented by some important points of caution.
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First, “proving” that racism exists is not the same as dismantling it. An evidence-based response to systemic racism is important and can be assisted through data to help identify the systems, policies and practices that need to be challenged and dismantled. However, ending racism has to begin and end with political will. Data, while helpful in guiding policy focus, are not a shortcut to creating this will.
Mimi Onuoha, an artist and researcher whose work highlights the power dynamics behind data collection, explained most recently that, “The idea that structural racism can be proven and overcome by gathering just enough or the right kind of evidence is nothing more than a myth. Historically, it has rarely been the case.” She describes several instances in the course of United States history when evidence of racial inequalities was deemed an inconvenient truth and either ignored or destroyed.
Data can be a useful tool for exposing the symptoms of racism but should not be conflated with a solution that gets to the root cause. Like any tool, data have their limitations.
Second, data extraction can pose a threat to the rights of marginalised and racialised groups in and of itself. Data are not merely recorded or collected, they are produced. Data extraction infrastructures comprise multiple points of subjectivity: design, collection, analysis, interpretation and dissemination. All of these open the door to exploitation, and for this reason, data extraction and surveillance have been argued to constitute colonial means of control. Which begs the question of whether increasing these mechanisms really is how we should be fighting structural oppression.
In this context, we also need to consider the precedents we have where “more data” approaches backfired. Butler mentions this in her piece, but it is not properly grappled with. In the COVID-19 context, we can, for example, look at how, in South Korea, digital COVID tracking data has exacerbated hostility towards LGBTQ people, who are associated with higher risks of contracting the coronavirus. Surveillance itself can be a public health risk that disproportionately impacts marginalised racial groups.
The treatment of Roma and Sinti communities throughout Europe provides particularly striking examples of how collecting data on marginalised groups can stoke and even exacerbate prejudices. When a group of UK researchers set out to collect better data on Roma migrants to assess their social needs, missteps in data presentation and publication gave rise to a political outcry over an “influx” of migrants in UK cities.
When France’s interior minister sought to justify his campaign to deport Roma individuals in 2010, he cited “objective” crime data, arguing “this is not about stigmatising this or that population, but we cannot close our eyes to reality”.
These examples illustrate that data do not exist independently of the biases and stigmas that drive leaders and governments to take measures targeted at specific communities – the same data that are at one point collected with the best of intentions can later be misused or “spun”. In times of social and economic crisis, the risk that data become a tool for the powerful to scapegoat marginalised groups runs especially high.
Third, the positive impact of having more data needs to be assessed with great caution. The US has been collecting the type of data Butler suggests the EU needs more of for decades, but evidence of racism has failed to bring about the structural change that recent protests have been calling for.
In 1947, the Truman administration’s Committee on Civil Rights prepared a report documenting police violence against members of minority groups and particularly against Black people. In the 73 years since, the US has not suffered from a lack of data on police murdering Black people and people of colour – it has suffered a lack of political will to do something about it. As writer Colbert King commented in the Washington Post, “America knows, and has always known, about the problem.”
Centring racial justice work around calls for more equality data risks narrowing the focus of racial and social justice movements to those injustices that have been made “visible” using data. This will only be a small part of the power structures that marginalise communities. It also plays into an institutional culture that requires marginalised communities to “prove” their own marginalisation with ”objective science” before they are believed, thereby effectively keeping these oppressing structures in place.
Fourth, if any data is collected, racialised and marginalised groups should be in control of what gets collected and how. Data collection is a powerful tool. Until communities can construct their own data collection and analysis practices, data extraction and population-monitoring technologies risk becoming tools for racism.
This goes beyond using methodologies “guided by minority groups”, as Butler argues. Different groups are working to decolonise data science and create platforms for communities to construct their own narratives and stories. Building a more inclusive data science field does not start with collecting more data, it starts with redistributing power and redefining how data systems are regulated to serve the needs of marginalised groups.
A crucial first step in all of this, as Tawana Petty, the director of the Data Justice Program at the Detroit Community Technology Project, recently said, is to ask if the data collection is necessary in the first place. Just because we can collect it, does not mean we should.
Instead of focusing on extracting information from racialised and marginalised communities, it would make sense to make more resources available to existing community networks. Community-based data collection projects such as CRAN’s “Statistiques Populaires” initiative of the Representative Council of Black Associations in France and “Afrozensus” of the Berlin-based organisation Each One Teach One could serve as a model for other national and cross-border initiatives.
Data collection should not be viewed as a means to an end; instead, it is one of the structures that must be dismantled as part of a larger anti-racist project. Calling for more data as an essential part of the solution to inequality runs the risk of doing the opposite; if not done properly, it creates an opportunity to increase surveillance in the name of fighting racism.
Yes, Europe has a problem with systemic racism and, yes, we need to face this head on. But rather than running the risk of adding to existing systems of oppression, we should focus on dismantling the structures and practices that we know to be racist.
The views expressed in this article are the author’s own and do not necessarily reflect Al Jazeera’s editorial stance.