Nonprofits and charities around the world increasingly rely on data to pursue their mission. As digital technologies become part of our everyday lives, activists grow more adept at using data-driven solutions as well. With the help of emerging technologies, nonprofits can now identify new patterns, find alternative sources of information, strengthen their advocacy, and tell better stories about their work.
But working with data isn’t magically yielding positive impact, just because our mission is to do good in the world. Nonprofits need to think critically about the data they work with every step of the way. Does crucial information even exist to begin with? How was the data collected in the first place? Who is it shared with and how?
The following prompts are meant to provide some guidance on what questions might be worth considering at the onset of every data-driven project, as well as practical advice on how to move beyond reflections.
Does the data even exist?
A lot of the information that might be relevant for civil society’s work is out there and ready to be ‘harvested’, but crucial datasets are missing even from our data-saturated world. Data that would be essential to pursue civic missions often does not even exist to begin with — either because there is no interest in collecting the information, or because political will to make it available is absent.
As an example, back in the 90s hundreds of women and girls were murdered in Juarez, Mexico, causing significant international turmoil. But despite the heightened media attention, only a few convictions were made and the killings continued even after the trials were over.
Many argued that the lack of action was due to the fact that there was no systematic data collection about the cases, and that the victims were often misrepresented by mainstream media thanks to the rampant sexism in Mexican society.
To counter that, activist-researcher María Salguero created a map to log the killings exactly how they happened, after meticulously reviewing media reports, official bulletins and crowdsourced contributions. By doing so, she managed to provide the most accessible information on the subject to date, a database that is still used by journalists and activists across and beyond Mexico.
Salguero’s project demonstrates that civil society can indeed help fill these “data gaps” by collecting and structuring the missing information themselves. In fact, some of the most powerful civic initiatives were born out of an urgent need when official data didn’t exist —just think of human rights defenders creating evidence on war crimes or anti-corruption activists documenting the spending of political parties in election campaigns.
How is the data collected in the first place?
The world of data is often seen as something inherently objective, but in reality, our data-driven systems are never neutral —the same way none of our social systems and institutions are objective to begin with. Centuries of oppression and exclusion shaped our societies into what they look like today and this dynamic is reflected in our digital reality too.
As an example, child welfare agencies around the world increasingly engage in machine-enabled predictions to help forecast potential risks to children. By doing so, they collect a lot of information about communities where they believe violence and abuse against children is more probable to occur.
But just like in predictive policing, research shows that welfare agencies tend to disproportionately focus on poor and marginalized communities in their data collection, recreating centuries-old patterns of racism and stigma, and resulting in unnecessary interventions, family breakups, and life-long trauma.
The problem isn’t only that important decisions are now routinely left to machines which then recreate the biases of their human creators. It is equally troublesome that such data often turns into official statistics — information that civil society relies heavily on for their campaigning, research and services. In the case of predictive analytics, for instance, the overrepresentation of certain communities easily becomes a self-fulfilling prophecy, but the seemingly neutral ways in which the data was created makes it more difficult to question its legitimacy.
To mitigate these risks, nonprofits need to pay close attention to the human decisions and biases behind every data collection process, and challenge their assumptions about working with publicly available information and official statistics. Communities like the Responsible Data Forum can provide a great avenue to discuss and assess those risks and challenges, and come up with counter solutions and alternatives.
How is the data being shared?
How nonprofits share the data, what stories they tell with it, and who they manage to engage with their information matters as much as the ways in which the data has been collected or created. If the work is meant to reach wider, more diverse audiences, civil society’s storytelling practices need to become more diverse as well.
Luckily, we have a growing number of alternative approaches that can help move beyond mainstream ways of sharing data (like standard visualizations). Concepts like data physicalization or data visceralization challenge our assumptions about how we present information, emphasizing that human interaction with data should not be limited to the world of pixels and charts.
Consequently, civil society’s existing experiments with story-telling forms are becoming more and more advanced as well.
The Data Art project from the Central American Journalism Forum, for instance, offers novel ways of communicating messages through art and culture, creating alternative representations of journalistic databases on violence, disappearances and inequality. The Glass Room project is an interactive exhibition that challenges our understanding how technologies are changing our lives through showcasing the work of multiple artists. And the Library of Missing Datasets provides a real-life repository of datasets that are absent from our otherwise data-saturated world.
This is just a taste of how civil society can think more critically about the information they encounter within their work. Challenging our assumptions about how we work with data is not going to be enough to achieve impactful work, but it might be the right first step in the right direction.
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