Big Data Digital Humanitarianism

I’m writing a paper at the moment in which I explore the relationship between the promises of Big Data and how it actually plays out in a digital humanitarian context. My goal is to use the outcomes of my research at the Wilson Center last year to understand Big Data as a set of practices, as an epistemology, and as a social relation. I’m having trouble fleshing out the part where I talk about the current state of Big Data in digital humanitarianism research (i.e., the “literature review”), so I figured I’d dump those ideas into a blog post first, just to have some output that I can put into the paper later. In other words, what follows is a very rough, unedited solo brainstorming session!

First, some context. Digital humanitarianism is a relatively new field that develops technologies, collaboration techniques, and social-institutional frameworks to allow people from all over the world to contribute to humanitarian projects remotely. Through the use of technologies like OpenStreetMap, the Standby Task Force, Skype, Ushahidi, and Sahana, people like you and I can help produce, process, filter, and map data in humanitarian situations. The traditional humanitarian organizations that are moving in this direction include the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA), American Red Cross, US Agency for International Development (US AID) (not humanitarianism by some definitions), and FEMA is dabbling in it. Sometimes you’re contributing to these projects without even knowing it, as some digital humanitarian groups stream data from social networking sites like Twitter. When they do this, they can create massive, unstructured, complex datasets that are called “Big Data”. Big Data has been hailed as the solution to many problems, like (facetiously) science and the qualitative context of information.

Digital humanitarianism is one area in which conversations around Big Data are just starting to take off. Letouzé claims this “revolution” is “extremely recent (less than one decade old), extremely rapid (the growth is exponential), and immensely consequential for society, perhaps especially for developing countries” [emphasis mine]. Letouzé thus distinguishes a Big Data moment with “previous” ways of doing things by positing a clean historic break: at a relatively discrete point in time Big Data emerged as an exponentially-growing phenomenon that impacts society in fundamental ways[1]. At stake is, as UN OCHA puts it, “a better model for making humanitarian policy, whereby people determine their own priorities and then communicate them to those who would assist.” Despite the promise of digital humanitarianism being large numbers of people contributing large amounts of data (Big Data), this quote suggests that individual people are communicating directly with those in control of resource allocations. Along these lines,Ziemke posits the goal of digital humanitarianism is not to connect the many needy individuals with aid providers but instead provide institutions with “just the right piece of information that might save a life.” Notwithstanding these irreconcilable discrepancies, incorporating Big Data into workflows is said to help develop “data-driven decision-making processes” - which are contrasted with “experience and intuition” (Letouzé 2012, 12). This sentiment reflects the view put forward in the highly criticized article on Big Data by editor of Wired, Chris Anderson:

This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.

However, discussions of the relationship between Big Data and digital humanitarianism tend to be cautiously optimistic. Letouzé characterizes the challenges facing digital humanitarianism as falling into four broad categories: 1) privacy, 2) access/sharing, 3) extracting meaning from qualitative text, 4) apophenia, 4) detecting anomalies. Similar characterizations of challenges can be seen elsewhere, usually framing challenges in ways that make them addressable through technological means. This conceptualization sees “challenges” in terms of hindrances to the integration of Big Data and humanitarianism, and “progress” as moving toward increasing integration of Big Data into humanitarianism.

These early conversations suggest a Big Data future is imminent and value-neutral, with obstacles delegated to technicist solutions. This conceptualization stands at odds with research showing how technologies, data, and society are co-constitutive. Not only does the convergence of Big Data and humanitarianism depend on a particular social shaping of technologies and data, but Big Data itself embodies particular values, social relations, and epistemologies. In the next section of the literature review I will discuss which principles in particular help shed light on these aspects of Big Data.

My paper will explore these social and political implications of Big Data digital humanitarianism by drawing on a 7-month empirical research project using the extended case method, based at a public policy research institute. This institute has been involved in organizing research around policy opportunities and challenges of digital humanitarianism, and provided insights into how Big Data is integrated into digital humanitarianism in the formal humanitarian and disaster management sectors.

[1] Note that this article was written about “Big Data for Development”. I am including it in this discussion about humanitarianism because, while the two fields differ on their operations and intellectual histories, they share many of the same underlying assumptions. For instance, they share assumptions about who has resources and should deliver those to whom, they are traditionally based on economic principles of resource distribution, and it can be said that their humanitarian/development situations often result from inequalities in global political economy.