Category Archives: Digital Activism

From Russia with Love: A Match.com for Disaster Response

I’ve been advocating for the development of a “Match.com” for disaster response since early 2010. Such a platform would serve to quickly match hyperlocal needs with relevant resources available at the local and national level, thus facilitating and accelerating self-organization following major disasters. Why advocate for a platform modeled after an online dating website? Because self-organized mutual-aid is an important driver of community resilience.

Russian Bell

Obviously, self-organization is not dependent on digital technology. The word Rynda, for example, is an old Russian word for a “village bell” which was used by local communities to self-organize during emergencies. Interestingly, Rynda became a popular meme on social media during fires in 2010. As my colleague Gregory Asmolov notes in his brilliant new study, a Russian blogger at the time of the fires “posted an emotional open letter to Prime Minister Putin, describing the lack of action by local authorities and emergency services.” In effect, the blogger demanded a “return to an old tradition of self-organization in local communities,” subsequently exclaiming “bring back the Rynda!” This demand grew into a popular meme symbolizing the catastrophic failure of the formal system’s response to the massive fires.

At the time, my colleagues Gregory, Alexey Sidorenko & Glafira Parinos launched the Help Map above in an effort to facilitate self-organization and mutual aid. But as Gregory notes in his new study, “The more people were willing to help, the more difficult it was to coordinate the assistance and to match resources with needs.” Moreover, the Help Map continued to receive reports on needs and offers-of-help after the fires had subsided. To be sure, reports of flooding soon found their way to the map, for example. Gregory, Alexey, Glarifa and team thus launched “Virtual Rynda: The Help Atlas” to facilitate self-help in response to a variety of situations beyond sudden-onset crises.

“We believed that in order to develop the capacity and resilience to respond to crisis situations we would have to develop the potential for mutual aid in everyday life. This would rely on an idea that emergency and everyday-life situations were interrelated. While people’s motivation to help one another is lower during non-emergency situations, if you facilitate mutual aid in everyday life and allow people to acquire skills in using Internet-based technologies to help one another or in asking for assistance, this will help to create an improved capacity to fulfill the potential of mutual aid the next time a disaster happens. [...] The idea was that ICTs could expand the range within which the tolling of the emergency bell could be heard. Everyone could ‘ring’ the ‘Virtual Rynda’ when they needed help, and communication networks would magnify the sound until it reached those who could come and help.”

In order to accelerate and scale the matching of needs & resources, Gregory and team (pictured below) sought to develop a matchmaking algorithm. Rynda would ask users to specify what the need was, where (geographically) the need was located and when (time-wise) the need was requested. “On the basis of this data, computer-based algorithms & human moderators could match offers with requests and optimize the process of resource allocation.” Rynda also included personal profiles, enabling volunteers “to develop an online reputation and increase trust between those needing help and those who could offer assistance. Every volunteer profile included not only personal information, but also a history of the individual’s previous activities within the platform.” To this end, in addition to “Help Requests” & “Help Offers,” Rynda also included an entry for “Help Provided” to close the feedback loop.

Asmolov1

As Gregory acknowledges, the results were mixed but certainly interesting and insightful. “Most of the messages [posted to the Rynda platform dealt] with requests for various types of social help, like clothing and medical equipment for children, homes for orphans, people with limited capabilities, or families in need. [...]. Some requests from environmental NGOs were related to the mobilization of volunteers to fight against deforestation or to fight wildfires. [...]. In another case, a volunteer who responded to a request on the platform helped to transport resources to a family with many children living far from a big city. [...]. Many requests concern[ed] children or disabled people. In one case, Rynda found a volunteer who helped a young woman leave her flat for walks, something she could not do alone. In some cases, the platform helped to provide medicine.” In any event, an analysis of the needs posted to Rynda suggests that “the most needed resource is not the thing itself, but the capacity to take it to the person who needs it. Transportation becomes a crucial resource, especially in a country as big as Russia.”

Alas, “Despite the efforts to create a tool that would automatically match a request with a potential help provider, the capacity of the algorithm to optimize the allocation of resources was very limited.” To this end, like the Help Map initiative, digital volunteers who served as social moderators remained pivotal to the Virtual Ryndal platform. As Alexey notes, “We’ve never even got to the point of the discussion of more complex models of matching.” Perhaps Rynda should have included more structured categories to enable more automated-matching since the volunteer match-makers are simply not scalable. “Despite the intention that the ‘matchmaking’ algorithm would support the efficient allocation of resources between those in need and those who could help, the success of the ‘matchmaking’ depended on the work of the moderators, whose resources were limited. As a result, a gap emerged between the broad issues that the project could address and the limited resources of volunteers.”

To this end, Gregory readily admits that “the initial definition of the project as a general mutual aid platform may have been too broad and unspecific.” I agree with this diagnostic. Take the online dating platform Match.com for example. Match.com’s sole focus is online dating; Airbnb’s sole purpose is to match those looking for a place to stay with those offering their places; Uber’s sole purpose is matching those who need to get somewhere with a local car service. To this end, matching platform for mutual-aid may indeed been too broad—at least to begin with. Amazon began with books, but later diversified.

In any case, as Gregory rightly notes, “The relatively limited success of Rynda didn’t mean the failure of the idea of mutual aid. What [...] Rynda demonstrates is the variety of challenges encountered along the way of the project’s implementation.” To be sure, “Every society or community has an inherent potential mutual aid structure that can be strengthened and empowered. This is more visible in emergency situations; however, major mutual aid capacity building is needed in everyday, non-emergency situations.” Thanks to Gregory and team, future digital matchmakers can draw on the above insights and Rynda’s open source code when designing their own mutual-aid and self-help platforms.

For me, one of the key take-aways is the need for a scalable matching platform. Match.com would not be possible if the matching were done primarily manually. Nor would Match.com work as well if the company sought to match interests beyond the romantic domain. So a future Match.com for mutual-aid would need to include automated matching and begin with a very specific matching domain. 

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See also:

  • Using Waze, Uber, AirBnB, SeeClickFix for Disaster Response [link]
  • MatchApp: Next Generation Disaster Response App? [link]
  • A Marketplace for Crowdsourcing Crisis Response [link]

Results of the Crowdsourced Search for Malaysia Flight 370 (Updated)

Update: More than 3 million volunteers thus far have joined the crowdsourcing efforts to locate the missing Malaysian Airlines plane. These digital volunteers have viewed over a quarter-of-a-billion micro-maps and have tagged almost 3 million features in these satellite maps. Source of update.

Malaysian authorities have now gone on record to confirm that Flight 370 was hijacked, which reportedly explains why contact with the passenger jet abruptly ceased a week ago. The Search & Rescue operations now involve 13 countries around the world and over 100 ships, helicopters and airplanes. The costs of this massive operation must easily be running into the millions of dollars.

FlightSaR

Meanwhile, a free crowdsourcing platform once used by digital volunteers to search for Genghis Khan’s Tomb and displaced populations in Somalia (video below) has been deployed to search high-resolution satellite imagery for signs of the missing airliner. This is not the first time that crowdsourced satellite imagery analysis has been used to find a missing plane but this is certainly the highest profile operation yet, which may explain why the crowdsourcing platform used for the search (Tomnod) reportedly crashed for over a dozen of hours since the online search began. (Note that Zooniverse can easily handle this level of traffic). Click on the video below to learn more about the crowdsourced search for Genghis Khan and displaced peoples in Somalia.

NatGeoVideo

Having current, high-resolution satellite imagery is almost as good as having your own helicopter. So the digital version of these search operations includes tens of thousands of digital helicopters, whose virtual pilots are covering over 2,000 square miles of Thailand’s Gulf right from their own computers. They’re doing this entirely for free, around the clock and across multiple time zones. This is what Digital Humanitarians have been doing ever since the 2010 Haiti Earthquake, and most recently in response to Typhoon Yolanda.

Tomnod has just released the top results of the crowdsourced digital search efforts, which are displayed in the short video below. Like other microtasking platforms, Tomnod uses triangulation to calculate areas of greatest consensus by the crowd. This is explained further here. Note: The example shown in the video is NOT a picture of Flight 370 but perhaps of an airborne Search & Rescue plane.

While looking for evidence of the missing airliner is like looking for the proverbial needle in a massive stack of satellite images, perhaps the biggest value-added of this digital search lays in identifying where the aircraft is most definitely not located—that is, approaching this crowdsourced operation as a process of elimination. Professional imagery analysts can very easily and quickly review images tagged by the crowd, even if they are mistakenly tagged as depicting wreckage. In other words, the crowd can provide the first level filter so that expert analysts don’t waste their time looking at thousands of images of bare oceans. Basically, if the mandate is to leave no stone unturned, then the crowd can do that very well.

In sum, crowdsourcing can reduce the signal to noise ratio so that experts can focus more narrowly on analyzing the potential signals. This process may not be perfect just yet but it can be refined and improved. (Note that professionals also get it wrong, like Chinese analysts did with this satellite image of the supposed Malaysian airliner).

If these digital efforts continue and Flight 370 has indeed been hijacked, then this will certainly be the first time that crowdsourced satellite imagery analysis is used to find a hijacked aircraft. The latest satellite imagery uploaded by Tomnod is no longer focused on bodies of water but rather land. The blue strips below (left) is the area that the new satellite imagery covers.

Tomnod New Imagery 2

Some important questions will need to be addressed if this operation is indeed extended. What if the hijackers make contact and order the cessation of all offline and online Search & Rescue operations? Would volunteers be considered “digital combatants,” potentially embroiled in political conflict in which the lives of 227 hostages are at stake?

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Note: The Google Earth containing the top results of the search is available here.

See also: Analyzing Tweets on Malaysia Flight #MH370 [link]

Yes, I’m Writing a Book (on Digital Humanitarians)

I recently signed a book deal with Taylor & Francis Press. The book, which is tentatively titled “Digital Humanitarians: How Big Data is Changing the Face of Disaster Response,” is slated to be published next year. The book will chart the rise of digital humanitarian response from the Haiti Earthquake to 2015, highlighting critical lessons learned and best practices. To this end, the book will draw on real-world examples of digital humanitarians in action to explain how they use new technologies and crowdsourcing to make sense of “Big (Crisis) Data”. In sum, the book will describe how digital humanitarians & humanitarian technologies are together reshaping the humanitarian space and what this means for the future of disaster response. The purpose of this book is to inspire and inform the next generation of (digital) humanitarians while serving as a guide for established humanitarian organizations & emergency management professionals who wish to take advantage of this transformation in humanitarian response.

2025

The book will thus consolidate critical lessons learned in digital humanitarian response (such as the verification of social media during crises) so that members of the public along with professionals in both international humanitarian response and domestic emergency management can improve their own relief efforts in the face of “Big Data” and rapidly evolving technologies. The book will also be of interest to academics and students who wish to better understand methodological issues around the use of social media and user-generated content for disaster response; or how technology is transforming collective action and how “Big Data” is disrupting humanitarian institutions, for example. Finally, this book will also speak to those who want to make a difference; to those who of you who may have little to no experience in humanitarian response but who still wish to help others affected during disasters—even if you happen to be thousands of miles away. You are the next wave of digital humanitarians and this book will explain how you can indeed make a difference.

The book will not be written in a technical or academic writing style. Instead, I’ll be using a more “storytelling” form of writing combined with a conversational tone. This approach is perfectly compatible with the clear documentation of critical lessons emerging from the rapidly evolving digital humanitarian space. This conversational writing style is not at odds with the need to explain the more technical insights being applied to develop next generation humanitarian technologies. Quite on the contrary, I’ll be using intuitive examples & metaphors to make the most technical details not only understandable but entertaining.

While this journey is just beginning, I’d like to express my sincere thanks to my mentors for their invaluable feedback on my book proposal. I’d also like to express my deep gratitude to my point of contact at Taylor & Francis Press for championing this book from the get-go. Last but certainly not least, I’d like to sincerely thank the Rockefeller Foundation for providing me with a residency fellowship this Spring in order to accelerate my writing.

I’ll be sure to provide an update when the publication date has been set. In the meantime, many thanks for being an iRevolution reader!

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The Best of iRevolution in 2013

iRevolution crossed the 1 million hits mark in 2013, so big thanks to iRevolution readers for spending time here during the past 12 months. This year also saw close to 150 new blog posts published on iRevolution. Here is a short selection of the Top 15 iRevolution posts of 2013:

How to Create Resilience Through Big Data
[Link]

Humanitarianism in the Network Age: Groundbreaking Study
[Link]

Opening Keynote Address at CrisisMappers 2013
[Link]

The Women of Crisis Mapping
[Link]

Data Protection Protocols for Crisis Mapping
[Link]

Launching: SMS Code of Conduct for Disaster Response
[Link]

MicroMappers: Microtasking for Disaster Response
[Link]

AIDR: Artificial Intelligence for Disaster Response
[Link]

Social Media, Disaster Response and the Streetlight Effect
[Link]

Why the Share Economy is Important for Disaster Response
[Link]

Automatically Identifying Fake Images on Twitter During Disasters
[Link]

Why Anonymity is Important for Truth & Trustworthiness Online
[Link]

How Crowdsourced Disaster Response Threatens Chinese Gov
[Link]

Seven Principles for Big Data and Resilience Projects
[Link]

#NoShare: A Personal Twist on Data Privacy
[Link]

I’ll be mostly offline until February 1st, 2014 to spend time with family & friends, and to get started on a new exciting & ambitious project. I’ll be making this project public in January via iRevolution, so stay tuned. In the meantime, wishing iRevolution readers a very Merry Happy Everything!

santahat

Early Results of MicroMappers Response to Typhoon Yolanda (Updated)

We have completed our digital humanitarian operation in the Philippines after five continuous days with MicroMappers. Many, many thanks to all volunteers from all around the world who donated their time by clicking on tweets and images coming from the Philippines. Our UN OCHA colleagues have confirmed that the results are being shared widely with their teams in the field and with other humanitarian organizations on the ground. More here.

ImageClicker

In terms of preliminary figures (to be confirmed):

  • Tweets collected during first 48 hours of landfall = ~230,000
  • Tweets automatically filtered for relevancy/uniqueness = ~55,000
  • Tweets clicked using the TweetClicker = ~ 30,000
  • Relevant tweets triangulated using TweetClicker = ~3,800
  • Triangulated tweets published on live Crisis Map = ~600
  • Total clicks on TweetClicker = ~ 90,000
  • Images clicked using the ImageClicker = ~ 5,000
  • Relevant images triangulated using TweetClicker = ~1,200
  • Triangulated images published on live Crisis Map = ~180
  • Total clicks on ImageClicker = ~15,000
  • Total clicks on MicroMappers (Image + Tweet Clickers) = ~105,000

Since each single tweet and image uploaded to the Clickers was clicked on by (at least) three individual volunteers for quality control purposes, the number of clicks is three times the total number of tweets and images uploaded to the respective clickers. In sum, digital humanitarian volunteers have clocked a grand total of ~105,000 clicks to support humanitarian operations in the Philippines.

While the media has largely focused on the technology angle of our digital humanitarian operation, the human story is for me the more powerful message. This operation succeeded because people cared. Those ~105,000 clicks did not magically happen. Each and every single one of them was clocked by humans, not machines. At one point, we had over 300 digital volunteers from the world over clicking away at the same time on the TweetClicker and more than 200 on the ImageClicker. This kind of active engagement by total strangers—good “digital Samaritans”—explains why I find the human angle of this story to be the most inspiring outcome of MicroMappers. “Crowdsourcing” is just a new term for the old saying “it takes a village,” and sometimes it takes a digital village to support humanitarian efforts on the ground.

Until recently, when disasters struck in faraway lands, we would watch the news on television wishing we could somehow help. That private wish—that innate human emotion—would perhaps translate into a donation. Today, not only can you donate cash to support those affected by disasters, you can also donate a few minutes of your time to support the operational humanitarian response on the ground by simply clicking on MicroMappers. In other words, you can translate your private wish into direct, online public action, which in turn translates into supporting offline collective action in the disaster-affected areas.

Clicking is so simple that anyone with Internet access can help. We had high schoolers in Qatar clicking away, fire officers in Belgium, graduate students in Boston, a retired couple in Kenya and young Filipinos clicking away. They all cared and took the time to try and help others, often from thousands of miles away. That is the kind of world I want to live in. So if you share this vision, then feel free to join the MicroMapper list-serve.

Yolanda TweetClicker4

Considering that MicroMappers is still very much under development, we are all pleased with the results. There were of course many challenges; the most serious was the CrowdCrafting server which hosts our Clickers. Unfortunately, that server was not able to handle the load and traffic generated by digital volunteers. So their server crashed twice and also slowed our Clickers to a complete stop at least a dozen times during the past five days. At times, it would take 10-15 seconds for a new tweet or image to load, which was frustrating. We were also limited by the number of tweets and images we could upload at any given time, usually ~1,500 at most. Any larger load would seriously slow down the Clickers. So it is rather remarkable that digital volunteers managed to clock more than 100,000 clicks given the repeated interruptions. 

Besides the server issue, the other main bottleneck was the geo-location of the ~30,000 tweets and ~5,000 images tagged using the Clickers. We do have a Tweet and Image GeoClicker but these were not slated to launch until next week at CrisisMappers 2013, which meant they weren’t ready for prime time. We’ll be sure to launch them soon. Once they are operational, we’ll be able to automatically push triangulated tweets and images from the Tweet and Image Clickers directly to the corresponding GeoClickers so volunteers can also aid humanitarian organizations by mapping important tweets and images directly.

There’s a lot more that we’ve learned throughout the past 5 days and much room for improvement. We have a long list of excellent suggestions and feedback from volunteers and partners that we’ll be going through starting tomorrow. The most important next step is to get a more powerful server that can handle a lot more load and traffic. We’re already taking action on that. I have no doubt that our clicks would have doubled without the server constraints.

For now, though, BIG thanks to the SBTF Team and in particular Jus McKinnon, the QCRI et al team, in particular Ji Lucas, Hemant Purohit and Andrew Ilyas for putting in very, very long hours, day in and day out on top of their full-time jobs and studies. And finally, BIG thanks to the World Wide Crowd, to all you who cared enough to click and support the relief operations in the Philippines. You are the heroes of this story.

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Digital Humanitarians: From Haiti Earthquake to Typhoon Yolanda

We’ve been able to process and make sense of a quarter of a million tweets in the aftermath of Typhoon Yolanda. Using both AIDR (still under development) and Twitris, we were able to collect these tweets in real-time and use automated algorithms to filter for both relevancy and uniqueness. The resulting ~55,000 tweets were then uploaded to MicroMappers (still under development). Digital volunteers from the world over used this humanitarian technology platform to tag tweets and now images from the disaster (click image below to enlarge). At one point, volunteers tagged some 1,500 tweets in just 10 minutes. In parallel, we used machine learning classifiers to automatically identify tweets referring to both urgent needs and offers of help. In sum, the response to Typhoon Yolanda is the first to make full use of advanced computing, i.e., both human computing and machine computing to make sense of Big (Crisis) Data.

ImageClicker YolandaPH

We’ve come a long way since the tragic Haiti Earthquake. There was no way we would’ve been able to pull off the above with the Ushahidi platform. We weren’t able to keep up with even a few thousand tweets a day back then, not to mention images. (Incidentally, MicroMappers can also be used to tag SMS). Furthermore, we had no trained volunteers on standby back when the quake struck. Today, not only do we have a highly experienced network of volunteers from the Standby Volunteer Task Force (SBTF) who serve as first (digital) responders, we also have an ecosystem of volunteers from the Digital Humanitarian Network (DHN). In the case of Typhoon Yolanda, we also had a formal partner, the UN Office for the Coordination of Humanitarian Affairs (OCHA), that officially requested digital humanitarian support. In other words, our efforts are directly in response to clearly articulated information needs. In contrast, the response to Haiti was “supply based” in that we simply pushed out all information that we figured might be of use to humanitarian responders. We did not have a formal partner from the humanitarian sector going into the Haiti operation.

Yolanda Prezi

What this new digital humanitarian operation makes clear is that preparedness, partnerships & appropriate humanitarian technology go a long way to ensuring that our efforts as digital humanitarians add value to the field-based operations in disaster zones. The above Prezi by SBTF co-founder Anahi (click on the image to launch the presentation) gives an excellent overview of how these digital humanitarian efforts are being coordinated in response to Yolanda. SBTF Core Team member Justine Mackinnon is spearheading the bulk of these efforts.

While there are many differences between the digital response to Haiti and Yolanda, several key similarities have also emerged. First, neither was perfect, meaning that we learned a lot in both deployments; taking a few steps forward, then a few steps back. Such is the path of innovation, learning by doing. Second, like our use of Skype in Haiti, there’s no way we could do this digital response work without Skype. Third, our operations were affected by telecommunications going offline in the hardest hit areas. We saw an 18.7% drop in relevant tweets on Saturday compared to the day before, for example. Fourth, while the (very) new technologies we are deploying are promising, they are still under development and have a long way to go. Fifth, the biggest heroes in response to Haiti were the volunteers—both from the Haitian Diaspora and beyond. The same is true of Yolanda, with hundreds of volunteers from the world over (including the Philippines and the Diaspora) mobilizing online to offer assistance.

A Filipino humanitarian worker in Quezon City, Philippines, for example, is volunteering her time on MicroMappers. As is customer care advisor from Eurostar in the UK and a fire officer from Belgium who recruited his uniformed colleagues to join the clicking. We have other volunteer Clickers from Makati (Philippines), Cape Town (South Africa), Canberra & Gold Coast (Australia), Berkeley, Brooklyn, Citrus Heights & Hinesburg (US), Kamloops (Canada), Paris & Marcoussis (France), Geneva (Switzerland), Sevilla (Spain), Den Haag (Holland), Munich (Germany) and Stokkermarke (Denmark) to name just a few! So this is as much a human story is it is one about technology. This is why online communities like MicroMappers are important. So please join our list-serve if you want to be notified when humanitarian organizations need your help.

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#NoShare: A Personal Twist on Data Privacy

Countless computers worldwide automatically fingerprint our use of social media around the clock without our knowledge or consent. So we’re left with the following choice: stay digital and face the Eye of Sauron, or excommunicate ourselves from social media and face digital isolation from society. I’d chose the latter were it not for the life-saving role that social media can play during disasters. So what if there were a third way? An alternative that enabled us to use social media without being fed to the machines. Imagine if the choice were ours. My PopRock Fellows (PopTech & Rockefeller Foundation) and I are pondering this question within the context of ethical community-driven resilience in the era Big Data.

privacy_image

One result of this pondering is the notion of #noshare or #ns hashtag. We propose using this hashtag on anything that we don’t want sensed and turned into fodder for the machines. This could include Facebook updates, tweets, emails, SMS, post cards, cars, buildings and even our physical selves. Buildings, for example, are increasingly captured by cameras on orbiting satellites and also by high-resolution cameras fixed to cars used for Google Streetview.

The #noshare hashtag is a humble attempt at regaining some agency over the machines—and yes the corporations and governments using said machines. To this end, #noshare is a social hack that seeks to make a public statement and establish a new norm: the right to be social without being sensed or exploited without our knowledge or consent. While traditional privacy may be dead, most of us know the difference between right and wrong. This may foster positive social pressure to respect the use of #noshare.

Think of #ns hashtag as drawing a line in the sand. When you post a public tweet and want that tweet to serve the single purpose of read-only by humans, then add #noshare. This tag simply signals the public sphere that your tweet is for human consumption only, and not to be used by machines; not for download, retweet, copying, analysis, sensing, modeling or prediction. Your use of #noshare regardless of the medium represents your public vote for trust & privacy; a vote for tuning this hashtag into a widespread social norm.

Screen Shot 2013-09-15 at 5.24.57 AM

Of course, this #noshare norm is not enforceable in a traditional sense. This means that one could search for, collect and analyze all tweets with the #noshare or #ns hashtag. We’re well aware of this “Barbara Streisand effect” and there’s nothing we can do about it just yet. But the point is to draw a normative line in the sand, to create a public and social norm that provokes strong public disapproval when people violate the #ns principle. What if this could become a social norm? What if positive social pressure could make it unacceptable to violate this norm? Could this create a deterrence effect?

Either way, the line between right and wrong would be rendered publicly explicit. There would thus be no excuse: any analysis, sensing, copying, etc., of #ns tweets would be the result of a human decision to willingly violate the public norm. This social hack would make it very easy for corporations and governments to command their data mining algorithms to ignore all our digital fingerprints that use the #ns hashtag. Crossing the #noshare line would thus provide basis for social action against the owners of the machines in question. Social pressure is favorable to norm creation. Could #ns eventually become part of a Creative Commons type license?

Obviously, #ns tagged content does not mean that content should not be made public. Contented tagged with #ns is meant to be public, but only for the human public and not for computers to store and analyze. The point is simple: we want the option of being our public digital selves without being mined, sensed and analyzed by machines without our knowledge and consent. In sum, #noshare is an awareness raising initiative that seeks to educate the public about our increasingly sensed environment. Indeed, Big Data = Big Sensing.

We suggest that #ns may return a sense of moral control to individuals, a sense of trust and local agency. These are important elements for social capital and resilience, for ethical, community-driven resilience. If this norm gains traction, we may be able to code this norm into social media platforms. In sum, sensing is not bad; sensing of social media during disasters can save lives. But the decision of whether or not to be sensed should be the decision of the individual.

My PopRock Fellows and I are looking for feedback on this proposal. We’re aware of some of the pitfalls, but are we missing anything? Are there ways to strengthen this campaign? Please let us know in the comments section below. Thank you!

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Acknowledgements: Many thanks to PopRock Fellows Gustavo, Amy, Kate, Claudia and Jer for their valuable feedback on earlier versions of this post. 

Yes, But Resilience for Whom?

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.

Future_PredictiveCoding

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.

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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.

Why Digital Social Capital Matters for Disaster Resilience and Response

Recent empirical studies have clearly demonstrated the importance of offline social capital for disaster resilience and response. I’ve blogged about some of this analysis here and here. Social capital is typically described as those “features of social organizations, such as networks, norms, and trust, that facilitate action and cooperation for mutual benefit.” In other words, social capital increases a group’s capacity for collective action and thus self-organization, which is a key driver of disaster resilience. What if those social organizations were virtual and the networks digital? Would these online communities “generate digital social capital”? And would this digital social capital have any impact on offline social capital, collective action and resilience?

Social Capital

A data-driven study published recently, “Social Capital and Pro-Social Behavior Online and Offline” (PDF), presents some fascinating insights. The study, carried out by Constantin M. Bosancianu, Steve Powell and Esad Bratovi, draws on their survey of 1,912 Internet users in Bosnia & Herzegovina, Croatia and Serbia. The authors specifically consider two types of social capital: bonding social capital and bridging social capital. “

“Bridging social capital is described as inclusive, fostered in networks where membership is not restricted to a particular group defined by strict racial, class, linguistic or ethnic criteria.  Regular interactions inside these networks would gradually build norms of generalized trust and reciprocity at the individual level. These relationships [...] are able to offer the individual access to new information but are not very adept in providing emotional support in times of need.”

“Bonding social capital, on the other hand, is exclusive, fostered in tight-knit networks of family members and close friends. Although the degree of information redundancy in these networks is likely high (as most members occupy the same social space), they provide [...] the “sociological superglue” which gets members through tough emotional stages in their lives.”

The study’s findings reveal that online and offline social capital were correlated with each other. More specifically, online bridging social capital was closely correlated with offline bridging social capital, while online binding social capital was closely correlated with offline binding social capital. Perhaps of most interest with respect to disaster resilience, the authors discovered that “offline bridging social capital can benefit from online interactions.”

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Big Data: Sensing and Shaping Emerging Conflicts

The National Academy of Engineering (NAE) and US Institute of Peace (USIP) co-organized a fascinating workshop on “Sensing & Shaping Emerging Conflicts” in November 2012. I had the pleasure of speaking at this workshop, the objective of which was to “identify major opportunities and impediments to providing better real-time information to actors directly involved in situations that could lead to deadly violence.” We explored “several scenarios of potential violence drawn from recent country cases,” and “considered a set of technologies, applications and strategies that have been particularly useful—or could be, if better adapted for conflict prevention.” 

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The workshop report was finally published this week. If you don’t have time to leaf through the 40+page study, then the following highlights may be of interest. One of the main themes to emerge was the promise of machine learning (ML), a branch of Artificial Intelligence (AI). These approaches “continue to develop and be applied in un-anticipated ways, [...] the pressure from the peacebuilding community directed at technology developers to apply these new technologies to the cause of peace could have tremendous benefits.” On a personal note, this is one of the main reasons I joined the Qatar Computing Research Institute (QCRI); namely to apply the Institute’s expertise in ML and AI to the cause of peace, development and disaster relief.

“As an example of the capabilities of new technologies, Rafal Rohozinski, principal with the SecDev Group, described a sensing exercise focused on Syria. Using social media analytics, his group has been able to identify the locations of ceasefire violations or regime deployments within 5 to 15 minutes of their occurrence. This information could then be passed to UN monitors and enable their swift response. In this way, rapid deductive cycles made possible through technology can contribute to rapid inductive cycles in which short-term predictions have meaningful results for actors on the ground. Further analyses of these events and other data also made it possible to capture patterns not seen through social media analytics. For example, any time regime forces moved to a particular area, infrastructure such as communications, electricity, or water would degrade, partly because the forces turned off utilities, a normal practice, and partly because the movement of heavy equipment through urban areas caused electricity systems go down. The electrical grid is connected to the Internet, so monitoring of Internet connections provided immediate warnings of force movements.”

This kind of analysis may not be possible in many other contexts. To be sure, the challenge of the “Digital Divide” is particularly pronounced vis-a-vis the potential use of Big Data for sensing and shaping emerging conflicts. That said, my colleague Duncan Watts “clarified that inequality in communications technology is substantially smaller than other forms of inequality, such as access to health care, clean water, transportation, or education, and may even help reduce some of these other forms of inequality. Innovation will almost always accrue first to the wealthier parts of the world, he said, but inequality is less striking in communications than in other areas.” By 2015, for example, Sub-Saharan Africa will have more people with mobile network access than with electricity at home.

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My colleague Chris Spence from NDI also presented at the workshop. He noted the importance of sensing the positive and not just the negative during an election. “In elections you want to focus as much on the positive as you do on the negative and tell a story that really does convey to the public what’s actually going on and not just a … biased sample of negative reports.” Chris also highlighted that “one problem with election monitoring is that analysts still typically work with the software tools they used in the days of manual reporting rather than the Web-based tools now available. There’s an opportunity that we’ve been trying to solve, and we welcome help.” Building on our expertise in Machine Learning and Artificial Intelligence, my QCRI colleagues and I want to develop classifiers that automatically categorize large volumes of crowdsourced election reports. So I’m exploring this further with Chris & NDI. Check out the Artificial Intelligence for Monitoring Elections (AIME) project for more information.

One of the most refreshing aspects of the day-long workshop was the very clear distinction made between warning and response. As colleague Sanjana Hattotuwa cautioned: “It’s an open question whether some things are better left unsaid and buried literally and metaphorically.”  Duncan added that, “The most important question is what to do with information once it has been gathered.” Indeed, “Simply giving people more information doesn’t necessarily lead to a better outcome, although some-times it does.” My colleague Dennis King summed it up very nicely, “Political will is not an icon on your computer screen… Generating political will is the missing factor in peacebuilding and conflict resolution.”

In other words, “the peacebuilding community often lacks actionable strategies to convert sensing into shaping,” as colleague Fred Tipson rightly noted. Libbie Prescott, who served as strategic advisor to the US Secretary of State and participated in the workshop, added: “Policymakers have preexisting agendas, and just presenting them with data does not guarantee a response.” As my colleague Peter Walker wrote in a book chapter published way back in 1992, “There is little point in investing in warning systems if one then ignores the warnings!” To be clear, “early warning should not be an end in itself; it is only a tool for preparedness, prevention and mitigation with regard to disasters, emergencies and conflict situations, whether short or long term ones. [...] The real issue is not detecting the developing situation, but reacting to it.”

Now Fast froward to 2013: OCHA just published this groundbreaking report confirming that “early warning signals for the Horn of Africa famine in 2011 did not produce sufficient action in time, leading to thousands of avoidable deaths. Similarly, related research has shown that the 2010 Pakistan floods were predictable.” As DfID notes in this 2012 strategy document, “Even when good data is available, it is not always used to inform decisions. There are a number of reasons for this, including data not being available in the right format, not widely dispersed, not easily accessible by users, not being transmitted through training and poor information management. Also, data may arrive too late to be able to influence decision-making in real time operations or may not be valued by actors who are more focused on immediate action” (DfID)So how do we reconcile all this with Fred’s critical point: “The focus needs to be on how to assist the people involved to avoid the worst consequences of potential deadly violence.”

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The fact of the matter is that this warning-response gap in the field of conflict prevention is over 20 years old. I have written extensively about the warning-response problem here (PDF) and here (PDF), for example. So this challenge is hardly a new one, which explains why a number of innovative and promising solutions have been put forward of the years, e..g, the decentralization of conflict early warning and response. As my colleague David Nyheim wrote five years ago:

A state-centric focus in conflict management does not reflect an understanding of the role played by civil society organisations in situations where the state has failed. An external, interventionist, and state-centric approach in early warning fuels disjointed and top down responses in situations that require integrated and multilevel action.” He added: “Micro-level responses to violent conflict by ‘third generation early warning systems’ are an exciting development in the field that should be encouraged further. These kinds of responses save lives.”

This explains why Sanjana is right when he emphasizes that “Technology needs to be democratized [...], made available at the lowest possible grassroots level and not used just by elites. Both sensing and shaping need to include all people, not just those who are inherently in a position to use technology.” Furthermore, Fred is spot on when he says that “Technology can serve civil disobedience and civil mobilization [...] as a component of broader strategies for political change. It can help people organize and mobilize around particular goals. It can spread a vision of society that contests the visions of authoritarian.”

In sum, As Barnett Rubin wrote in his excellent book (2002) Blood on the Doorstep: The Politics of Preventive Action, “prevent[ing] violent conflict requires not merely identifying causes and testing policy instruments but building a political movement.” Hence this 2008 paper (PDF) in which I explain in detail how to promote and facilitate technology-enabled civil resistance as a form of conflict early response and violence prevention.

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See Also:

  • Big Data for Conflict Prevention [Link]