I sense a little bit of history repeating, and not the good kind. About ten years ago, I was deeply involved in the field of conflict early warning and response. Eventually, I realized that the systems we were designing and implementing excluded at-risk communities even though the rhetoric had me believe they were instrumented to protect them. The truth is that these information systems were purely extractive and ultimately did little else than fill the pockets of academics who were hired as consultants to develop these early warning systems.
The prevailing belief amongst these academics was (and still is) that large datasets and advanced quantitative methodologies can predict the escalation of political tensions and thus impede violence. To be sure, “these systems have been developed in advanced environments where the intention is to gather data so as to predict events in distant places. This leads to a division of labor between those who ‘predict’ and those ‘predicted’ upon” (Cited Meier 2008, PDF).
Those who predict assume their sophisticated remote sensing systems will enable them to forecast and thus prevent impending conflict. Those predicted upon don’t even know these systems exist. The sum result? Conflict early warning systems have failed miserably at forecasting anything, let alone catalyzing preventive action or empowering local communities to get out of harm’s way. Conflict prevention is inherently political, and “political will is not an icon on your computer screen” (Cited in Meier 2013).
In Toward a Rational Society (1970), the German philosopher Jürgen Habermas describes “the colonization of the public sphere through the use of instrumental technical rationality. In this sphere, complex social problems are reduced to technical questions, effectively removing the plurality of contending perspectives” (Cited in Meier 2006, PDF). This instrumentalization of society depoliticized complex social problems like conflict and resilience into terms that are susceptible to technical solutions formulated by external experts. The participation of local communities thus becomes totally unnecessary to produce and deliver these technical solutions. To be sure, the colonization of the public sphere crowds out both local knowledge and participation.
We run this risk of repeating these mistakes with respect the discourse on community resilience. While we speak of community resilience, we gravitate towards the instrumentalization of communities using Big Data, which is largely conceived as a technical challenge of real-time data sensing and optimization. This external, top-down approach bars local participation. The depoliticization of resilience also hides the fact that “every act of measurement is an act marked by the play of powerful relations” (Cited Meier 2013b). To make matters worse, these measurements are almost always taken without the subjects knowing, let alone their consent. And so we create the division between those who sense and those sensed upon, thereby fully excluding the latter, all in the name of building community resilience.
Acknowledgements: I raised the question “Resilience for whom?” during the PopTech and Rockefeller Foundation workshop on “Big Data & Community Resilience.” I am thus grateful to the organizers and fellows for informing my thinking and the motivation for this post.
This article is left wanting.
As a practitioner in the field, and a certified, over educated one at that, I submit the following on behalf of my colleagues in considering this information.
1. Community resilience is not about early warning. There is such a slight overlap in these two topics that any practitioner will tell you that real resilience is forged with small business, healthcare, and infrastructure-hardening. Early warning is nice – but it’s icing.
2. Big data – not really. Keep in mind most cities are small to moderately sized and towns are smaller that that! “Big data”, even if understood by all of us, is certainly not being utilized to make any type of decision. Practitioners use, and it’s quite effective, networking – getting out from behind the desk and learning the SWOTs of all our industries.
3. Finally, I turn to concent. I think you would find that most “sensing systems” are absent of one-way technology – we’re just not there yet. Currently, situational reporting is carried out by voluntary upward methods and analyzed/shared. I would challenge that nearly all information collected during a response is voluntary at some level. And when in the process of resiliency planning, the same us typically true.
WADR, this seems awfully disconnected – feel free to reach out to any of us practitioners for advice or input!
Many thanks for reading and for taking the time to comment.
Perhaps some context may be in order. Please note that I am speaking about the intersection of sensing technology and disaster resilience within the context of cities. Perhaps I should have placed the acknowledgements section at the top to provide said context.
On Big Data, you’d be surprised. I’ve spoken with several individuals who are at the forefront of instrumenting cities. My post is a response to these conversations and projects that are looking to inform (top-down) decision-making. These initiatives overlook data privacy, data protection and data access issues entirely. They also exclude local/community participation. As such, I do worry about the parallels with the early days of early warning/response systems. O
On sensing and one-way technology, again you’d be surprised by some of the sensors being developed and deployed (and linked to other datasets on income, taxes, etc) for the purposes of monitoring resilience. In any event, I am simply raising a concern giving what I am witnessing regarding the early conversations taking place on how Big Data is to be used to build community resilience.
Thanks again for sharing your concern as well.