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Conclusion: Putting Counterdata Science in its Place

Published onNov 02, 2022
Conclusion: Putting Counterdata Science in its Place

In the last few decades, we have developed a shocking degree of faith in the power of technology to solve our problems. In the face of neoliberal austerity, technology appears to be a cost-saving fix. Governments are adopting automated systems to allocate social services, to determine who gets a loan, or to judge who should be imprisoned. These are having devastating effects on minoritized populations – expanding corporate and government surveillance, exacerbating inequality, and fortifying mass incarceration.

For those of us who practice data science and data communication, it is important to resist the technosolutionist Kool-Aid from Silicon Valley: data are not going to "solve" matters of social inequality. More data doesn't readily translate to more action or more justice. On the contrary, the demand for more proof – more data collection, more analysis, more research – is often a terribly effective delay tactic employed by mainstream institutions to avoid taking meaningful action on social justice.

Against this backdrop, it is crucial for us to recognize that counterdata science is not a "solution" and it is not saving anyone or anything anytime soon. Rather, it is something much more humble. Counterdata science is a specific tactic of knowledge generation and consciousness building deployed amidst deeply unjust information ecosystems. Just as neither a documentary film nor a new policy nor an advocacy campaign can "solve" structural inequality, it is silly for us to expect that a database or data system – even a counter-database or counterdata-system – might do the same. What we might rather hope for (and tangibly organize for) is that a counterdata science project might participate in a collective concert of social action towards transformative change.

As I have been writing this book, I find myself coming back often to this statement from Paola Maldonado, an organizer with the Alliance for Monitoring and Mapping Feminicide in Ecuador:

We play a minimal role in this process, which is basically a defense, a fight for rights and for a dignified life for women and for the eradication of violence, but this is collective work, networked work, work that it has to continue expanding upwards, downwards, towards all the edges that we can give it.1

Paola is wise. She helps us see the multiplicity of meanings in our efforts. The Alianza's work producing feminicide data in Ecuador is both minimal and centrally important. It is a community defense – a way to refuse violence and work towards its eradication. But she positions their work relationally – within the many collectives and networks that work on the same issue but who may use different methods and serve different communities. All of that work is necessary. And all of the work that each of us does is necessary; individually insignificant, and yet increasingly powerful in aggregate. This is certainly true of the Latin American feminist movements, whose popular strength is a model for what can be accomplished by collective political action. Together we can extend further and further, towards all the edges, in order to realize a generative vision of a new world order: the remarkably non-radical idea that all women can and should live a life free from violence.

Comments
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Mariel Garcia-Montes:

I am struggling a little with where the book ends. Reading this chapter that puts counterdata science in its place, I can’t help but nod and agree. It is not a solution, it is a small part of policy and advocacy in this space. Yet we are here with the first ever written book about feminicide data activists because data science circles and policy researchers have long excluded them from the table to which they belong!

Considering it’s the conclusion, I wonder if it’s a good space to more overtly drive home the bold (and well argumented) claims that you build up throughout the book but especially in chapters 4, 5, and 6. It is true that counterdata science is not a solution, but I think an equally major point to be restated and celebrated is that feminicide data activists are in fact researchers and they are engaging in counterdata science.

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James Scott-Brown:

“What we might rather hope for (and tangibly organize for) is a counterdata science project that participates in …“

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James Scott-Brown:

Aren’t most automated decisions about loans made by private companies (such as banks), rather than governments?