Tag Archives: development

Using Big Data to Inform Poverty Reduction Strategies

My colleagues and I at QCRI are spearheading a new experimental Research and Development (R&D) project with the United Nations Development Program (UNDP) team in Cairo, Egypt. Colleagues at Harvard University, MIT and UC Berkeley have also joined the R&D efforts as full-fledged partners. The research question: can an analysis of Twitter traffic in Egypt tell us anything about changes in unemployment and poverty levels? This question was formulated with UNDP’s Cairo-based Team during several conversations I had with them in early 2013.

Egyptian Tweets

As is well known, a major challenge in the development space is the lack of access to timely socio-economic data. So the question here is whether alternative, non-traditional sources of information (such as social media) can provide a timely and “good enough” indication of changing trends. Thanks to our academic partners, we have access to hundreds of millions of Egyptian tweets (both historical and current) along with census and demographic data for ground-truth purposes. If the research yields robust results, then our UNDP colleagues could draw on more real-time data to complement their existing datasets, which may better inform some of their local poverty reduction and development strategies. This more rapid feedback loop could lead to faster economic empowerment for local communities in Egypt. Of course, there are many challenges to working with social data vis-a-vis representation and sample bias. But that is precisely why this kind of experimental research is important—to determine whether any of our results are robust to biases in phone ownership, twitter-use, etc.

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Could Lonely Planet Render World Bank Projects More Transparent?

That was the unexpected question that my World Bank colleague Johannes Kiess asked me the other day. I was immediately intrigued. So I did some preliminary research and offered to write up a blog post on the idea to solicit some early feedback. According to recent statistics, international tourist arrivals numbered over 1 billion in 2012 alone. Of this population, the demographic that Johannes is interested in comprises those intrepid and socially-conscious backpackers who travel beyond the capitals of developing countries. Perhaps the time is ripe for a new form of tourism: Tourism for Social Good.

tourism_socialmedia

There may be a real opportunity to engage a large crowd because travelers—and in particular the backpacker type—are smartphone savvy, have time on their hands, want to do something meaningful, are eager to get off the beaten track and explore new spaces where others do not typically trek. Johannes believes this approach could be used to map critical social infrastructure and/or to monitor development projects. Consider a simple smartphone app, perhaps integrated with existing travel guide apps or Tripadvisor. The app would ask travelers to record the quality of the roads they take (with the GPS of their smartphone) and provide feedback on the condition, e.g.,  bumpy, even, etc., every 50 miles or so.

They could be asked to find the nearest hospital and take a geotagged picture—a scavenger hunt for development (as Johannes calls it); Geocaching for Good? Note that governments often do not know exactly where schools, hospitals and roads are located. The app could automatically alert travelers of a nearby development project or road financed by the World Bank or other international donor. Travelers could be prompted to take (automatically geo-tagged) pictures that would then be forwarded to development organizations for subsequent visual analysis (which could easily be carried out using microtasking). Perhaps a very simple, 30-second, multiple-choice survey could even be presented to travelers who pass by certain donor-funded development projects. For quality control purposes, these pictures and surveys could easily be triangulated. Simple gamification features could also be added to the app; travelers could gain points for social good tourism—collect 100 points and get your next Lonely Planet guide for free? Perhaps if you’re the first person to record a road within the app, then it could be named after you (of course with a notation of the official name). Even Photosynth could be used to create panoramas of visual evidence.

The obvious advantage of using travelers against the now en vogue stakeholder monitoring approach is that they said bagpackers are already traveling there anyway and have their phones on them to begin with. Plus, they’d be independent third parties and would not need to be trained. This obviously doesn’t mean that the stakeholder approach is not useful. The travelers strategy would simply be complementary. Furthermore, this tourism strategy comes with several key challenges, such as the safety of backpackers who choose to take on this task, for example. But appropriate legal disclaimers could be put in place, so this challenge seems surmountable. In any event, Johannes, together with his colleagues at the World Bank (and I), hope to explore this idea of Tourism for Social Good further in the coming months.

In the meantime, we would be very grateful for feedback. What might we be overlooking? Would you use such an app if it were available? Where can we find reliable statistics on top backpacker destinations and flows?

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See also: 

  • What United Airlines can Teach the World Bank about Mobile Accountability [Link]

Zooniverse: The Answer to Big (Crisis) Data?

Both humanitarian and development organizations are completely unprepared to deal with the rise of “Big Crisis Data” & “Big Development Data.” But many still hope that Big Data is but an illusion. Not so, as I’ve already blogged here, here and here. This explains why I’m on a quest to tame the Big Data Beast. Enter Zooniverse. I’ve been a huge fan of Zooniverse for as long as I can remember, and certainly long before I first mentioned them in this post from two years ago. Zooniverse is a citizen science platform that evolved from GalaxyZoo in 2007. Today, Zooniverse “hosts more than a dozen projects which allow volunteers to participate in scientific research” (1). So, why do I have a major “techie crush” on Zooniverse?

Oh let me count the ways. Zooniverse interfaces are absolutely gorgeous, making them a real pleasure to spend time with; they really understand user-centered design and motivations. The fact that Zooniverse is conversent in multiple disciplines is incredibly attractive. Indeed, the platform has been used to produce rich scientific data across multiple fields such as astronomy, ecology and climate science. Furthermore, this citizen science beauty has a user-base of some 800,000 registered volunteers—with an average of 500 to 1,000 new volunteers joining every day! To place this into context, the Standby Volunteer Task Force (SBTF), a digital humanitarian group has about 1,000 volunteers in total. The open source Zooniverse platform also scales like there’s no tomorrow, enabling hundreds of thousands to participate on a single deployment at any given time. In short, the software supporting these pioneering citizen science projects is well tested and rapidly customizable.

At the heart of the Zooniverse magic is microtasking. If you’re new to microtasking, which I often refer to as “smart crowdsourcing,” this blog post provides a quick introduction. In brief, Microtasking takes a large task and breaks it down into smaller microtasks. Say you were a major (like really major) astro-nomy buff and wanted to tag a million galaxies based on whether they are spiral or elliptical galaxies. The good news? The kind folks at the Sloan Digital Sky Survey have already sent you a hard disk packed full of telescope images. The not-so-good news? A quick back-of-the-envelope calculation reveals it would take 3-5 years, working 24 hours/day and 7 days/week to tag a million galaxies. Ugh!

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But you’re a smart cookie and decide to give this microtasking thing a go. So you upload the pictures to a microtasking website. You then get on Facebook, Twitter, etc., and invite (nay beg) your friends (and as many strangers as you can find on the suddenly-deserted digital streets), to help you tag a million galaxies. Naturally, you provide your friends, and the surprisingly large number good digital Samaritans who’ve just show up, with a quick 2-minute video intro on what spiral and elliptical galaxies look like. You explain that each participant will be asked to tag one galaxy image at a time by simply by clicking the “Spiral” or “Elliptical” button as needed. Inevitably, someone raises their hands to ask the obvious: “Why?! Why in the world would anyone want to tag a zillion galaxies?!”

Well, only cause analyzing the resulting data could yield significant insights that may force a major rethink of cosmology and our place in the Universe. “Good enough for us,” they say. You breathe a sigh of relief and see them off, cruising towards deep space to bolding go where no one has gone before. But before you know it, they’re back on planet Earth. To your utter astonishment, you learn that they’re done with all the tagging! So you run over and check the data to see if they’re pulling your leg; but no, not only are 1 million galaxies tagged, but the tags are highly accurate as well. If you liked this little story, you’ll be glad to know that it happened in real life. GalaxyZoo, as the project was called, was the flash of brilliance that ultimately launched the entire Zooniverse series.

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No, the second Zooniverse project was not an attempt to pull an Oceans 11 in Las Vegas. One of the most attractive features of many microtasking platforms such as Zooniverse is quality control. Think of slot machines. The only way to win big is by having three matching figures such as the three yellow bells in the picture above (righthand side). Hit the jackpot and the coins will flow. Get two out three matching figures (lefthand side), and some slot machines may toss you a few coins for your efforts. Microtasking uses the same approach. Only if three participants tag the same picture of a galaxy as being a spiral galaxy does that data point count. (Of course, you could decide to change the requirement from 3 volunteers to 5 or even 20 volunteers). This important feature allows micro-tasking initiatives to ensure a high standard of data quality, which may explain why many Zooniverse projects have resulted in major scientific break-throughs over the years.

The Zooniverse team is currently running 15 projects, with several more in the works. One of the most recent Zooniverse deployments, Planet Four, received some 15,000 visitors within the first 60 seconds of being announced on BBC TV. Guess how many weeks it took for volunteers to tag over 2,000,0000 satellite images of Mars? A total of 0.286 weeks, i.e., forty-eight hours! Since then, close to 70,000 volunteers have tagged and traced well over 6 million Martian “dunes.” For their Andromeda Project, digital volunteers classified over 7,500 star clusters per hour, even though there was no media or press announce-ment—just one newsletter sent to volunteers. Zooniverse de-ployments also involve tagging earth-based pictures (in contrast to telescope imagery). Take this Serengeti Snapshot deployment, which invited volunteers to classify animals using photographs taken by 225 motion-sensor cameras in Tanzania’s Serengeti National Park. Volunteers swarmed this project to the point that there are no longer any pictures left to tag! So Zooniverse is eagerly waiting for new images to be taken in Serengeti and sent over.

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One of my favorite Zooniverse features is Talk, an online discussion tool used for all projects to provide a real-time interface for volunteers and coordinators, which also facilitates the rapid discovery of important features. This also allows for socializing, which I’ve found to be particularly important with digital humanitarian deployments (such as these). One other major advantage of citizen science platforms like Zooniverse is that they are very easy to use and therefore do not require extensive prior-training (think slot machines). Plus, participants get to learn about new fields of science in the process. So all in all, Zooniverse makes for a great date, which is why I recently reached out to the team behind this citizen science wizardry. Would they be interested in going out (on a limb) to explore some humanitarian (and development) use cases? “Why yes!” they said.

Microtasking platforms have already been used in disaster response, such as MapMill during Hurricane SandyTomnod during the Somali Crisis and CrowdCrafting during Typhoon Pablo. So teaming up with Zooniverse makes a whole lot of sense. Their microtasking software is the most scalable one I’ve come across yet, it is open source and their 800,000 volunteer user-base is simply unparalleled. If Zooniverse volunteers can classify 2 million satellite images of Mars in 48 hours, then surely they can do the same for satellite images of disaster-affected areas on Earth. Volunteers responding to Sandy created some 80,000 assessments of infrastructure damage during the first 48 hours alone. It would have taken Zooniverse just over an hour. Of course, the fact that the hurricane affected New York City and the East Coast meant that many US-based volunteers rallied to the cause, which may explain why it only took 20 minutes to tag the first batch of 400 pictures. What if the hurricane had hit a Caribbean instead? Would the surge of volunteers may have been as high? Might Zooniverse’s 800,000+ standby volunteers also be an asset in this respect?

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Clearly, there is huge potential here, and not only vis-a-vis humanitarian use-cases but development one as well. This is precisely why I’ve already organized and coordinated a number of calls with Zooniverse and various humanitarian and development organizations. As I’ve been telling my colleagues at the United Nations, World Bank and Humanitarian OpenStreetMap, Zooniverse is the Ferrari of Microtasking, so it would be such a big shame if we didn’t take it out for a spin… you know, just a quick test-drive through the rugged terrains of humanitarian response, disaster preparedness and international development. 

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Postscript: As some iRevolution readers may know, I am also collaborating with the outstanding team at  CrowdCrafting, who have also developed a free & open-source microtasking platform for citizen science projects (also for disaster response here). I see Zooniverse and CrowCrafting as highly syner-gistic and complementary. Because CrowdCrafting is still in early stages, they fill a very important gap found at the long tail. In contrast, Zooniverse has been already been around for half-a-decade and can caters to very high volume and high profile citizen science projects. This explains why we’ll all be getting on a call in the very near future. 

Opening World Bank Data with QCRI’s GeoTagger

My colleagues and I at QCRI partnered with the World Bank several months ago to develop an automated GeoTagger platform to increase the transparency and accountability of international development projects by accelerating the process of opening key development and finance data. We are proud to launch the first version of the GeoTagger platform today. The project builds on the Bank’s Open Data Initiatives promoted by former President, Robert Zoellick, and continued under the current leadership of Dr. Jim Yong Kim.

QCRI GeoTagger 1

The Bank has accumulated an extensive amount of socio-economic data as well as a massive amount of data on Bank-sponsored development projects worldwide. Much of this data, however, is not directly usable by the general public due to numerous data format, quality and access issues. The Bank therefore launched their “Mapping for Results” initiative to visualize the location of Bank-financed projects to better monitor development impact, improve aid effectiveness and coordination while enhancing transparency and social accountability. The geo-tagging of this data, however, has been especially time-consuming and tedious. Numerous interns were required to manually read through tens of thousands of dense World Bank project documentation, safeguard documents and results reports to identify and geocode exact project locations. But there are hundreds of thousands of such PDF documents. To make matters worse, these documents make seemingly “random” passing references to project locations, with no sign of any  standardized reporting structure whatsoever.

QCRI GeoTagger 2

The purpose of QCRI’s GeoTagger Beta is to automatically “read” through these countless PDF documents to identify and map all references to locations. GeoTagger does this using the World Bank Projects Data API and the Stanford Name Entity Recognizer (NER) & Alchemy. These tools help to automatically search through documents and identify place names, which are then geocoded using the Google GeocoderYahoo! Placefinder & Geonames and placed on a de-dicated map. QCRI’s GeoTagger will remain freely available and we’ll be making the code open source as well.

Naturally, this platform could be customized for many different datasets and organizations, which is why we’ve already been approached by a number of pro-spective partners to explore other applications. So feel free to get in touch should this also be of interest to your project and/or organization. In the meantime, a very big thank you to my colleagues at QCRI’s Big Data Analytics Center: Dr. Ihab Ilyas, Dr. Shady El-Bassuoni, Mina Farid and last but certainly not least, Ian Ye for their time on this project. Many thanks as well to my colleagues Johannes Kiess, Aleem Walji and team from the World Bank and Stephen Davenport at Development Gateway for the partnership.

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Big Data for Development: From Information to Knowledge Societies?

Unlike analog information, “digital information inherently leaves a trace that can be analyzed (in real-time or later on).” But the “crux of the ‘Big Data’ paradigm is actually not the increasingly large amount of data itself, but its analysis for intelligent decision-making (in this sense, the term ‘Big Data Analysis’ would actually be more fitting than the term ‘Big Data’ by itself).” Martin Hilbert describes this as the “natural next step in the evolution from the ‘Information Age’ & ‘Information Societies’ to ‘Knowledge Societies’ [...].”

Hilbert has just published this study on the prospects of Big Data for inter-national development. “From a macro-perspective, it is expected that Big Data informed decision-making will have a similar positive effect on efficiency and productivity as ICT have had during the recent decade.” Hilbert references a 2011 study that concluded the following: “firms that adopted Big Data Analysis have output and productivity that is 5–6 % higher than what would be expected given their other investments and information technology usage.” Can these efficiency gains be brought to the unruly world of international development?

To answer this question, Hilbert introduces the above conceptual framework to “systematically review literature and empirical evidence related to the pre-requisites, opportunities and threats of Big Data Analysis for international development.” Words, Locations, Nature and Behavior are types of data that are becoming increasingly available in large volumes.

“Analyzing comments, searches or online posts [i.e., Words] can produce nearly the same results for statistical inference as household surveys and polls.” For example, “the simple number of Google searches for the word ‘unemployment’ in the U.S. correlates very closely with actual unemployment data from the Bureau of Labor Statistics.” Hilbert argues that the tremendous volume of free textual data makes “the work and time-intensive need for statistical sampling seem almost obsolete.” But while the “large amount of data makes the sampling error irrelevant, this does not automatically make the sample representative.” 

The increasing availability of Location data (via GPS-enabled mobile phones or RFIDs) needs no further explanation. Nature refers to data on natural processes such as temperature and rainfall. Behavior denotes activities that can be captured through digital means, such as user-behavior in multiplayer online games or economic affairs, for example. But “studying digital traces might not automatically give us insights into offline dynamics. Besides these biases in the source, the data-cleaning process of unstructured Big Data frequently introduces additional subjectivity.”

The availability and analysis of Big Data is obviously limited in areas with scant access to tangible hardware infrastructure. This corresponds to the “Infra-structure” variable in Hilbert’s framework. “Generic Services” refers to the production, adoption and adaptation of software products, since these are a “key ingredient for a thriving Big Data environment.” In addition, the exploitation of Big Data also requires “data-savvy managers and analysts and deep analytical talent, as well as capabilities in machine learning and computer science.” This corresponds to “Capacities and Knowledge Skills” in the framework.

The third and final side of the framework represents the types of policies that are necessary to actualize the potential of Big Data for international develop-ment. These policies are divided into those that elicit a Positive Feedback Loops such as financial incentives and those that create regulations such as interoperability, that is, Negative Feedback Loops.

The added value of Big Data Analytics is also dependent on the availability of publicly accessible data, i.e., Open Data. Hilbert estimates that a quarter of US government data could be used for Big Data Analysis if it were made available to the public. There is a clear return on investment in opening up this data. On average, governments with “more than 500 publicly available databases on their open data online portals have 2.5 times the per capita income, and 1.5 times more perceived transparency than their counterparts with less than 500 public databases.” The direction of “causality” here is questionable, however.

Hilbert concludes with a warning. The Big Data paradigm “inevitably creates a new dimension of the digital divide: a divide in the capacity to place the analytic treatment of data at the forefront of informed decision-making. This divide does not only refer to the availability of information, but to intelligent decision-making and therefore to a divide in (data-based) knowledge.” While the advent of Big Data Analysis is certainly not a panacea,”in a world where we desperately need further insights into development dynamics, Big Data Analysis can be an important tool to contribute to our understanding of and improve our contributions to manifold development challenges.”

I am troubled by the study’s assumption that we live in a Newtonian world of decision-making in which for every action there is an automatic equal and opposite reaction. The fact of the matter is that the vast majority of development policies and decisions are not based on empirical evidence. Indeed, rigorous evidence-based policy-making and interventions are still very much the exception rather than the rule in international development. Why? “Account-ability is often the unhappy byproduct rather than desirable outcome of innovative analytics. Greater accountability makes people nervous” (Harvard 2013). Moreover, response is always political. But Big Data Analysis runs the risk de-politicize a problem. As Alex de Waal noted over 15 years ago, “one universal tendency stands out: technical solutions are promoted at the expense of political ones.” I hinted at this concern when I first blogged about the UN Global Pulse back in 2009.

In sum, James Scott (one of my heroes) puts it best in his latest book:

“Applying scientific laws and quantitative measurement to most social problems would, modernists believed, eliminate the sterile debates once the ‘facts’ were known. [...] There are, on this account, facts (usually numerical) that require no interpretation. Reliance on such facts should reduce the destructive play of narratives, sentiment, prejudices, habits, hyperbole and emotion generally in public life. [...] Both the passions and the interests would be replaced by neutral, technical judgment. [...] This aspiration was seen as a new ‘civilizing project.’ The reformist, cerebral Progressives in early twentieth-century American and, oddly enough, Lenin as well believed that objective scientific knowledge would allow the ‘administration of things’ to largely replace politics. Their gospel of efficiency, technical training and engineering solutions implied a world directed by a trained, rational, and professional managerial elite. [...].”

“Beneath this appearance, of course, cost-benefit analysis is deeply political. Its politics are buried deep in the techniques [...] how to measure it, in what scale to use, [...] in how observations are translated into numerical values, and in how these numerical values are used in decision making. While fending off charges of bias or favoritism, such techniques [...] succeed brilliantly in entrenching a political agenda at the level of procedures and conventions of calculation that is doubly opaque and inaccessible. [...] Charged with bias, the official can claim, with some truth, that ‘I am just cranking the handle” of a nonpolitical decision-making machine.”

See also:

  • Big Data for Development: Challenges and Opportunities [Link]
  • Beware the Big Errors of Big Data (by Nassim Taleb) [Link]
  • How to Build Resilience Through Big Data [Link]

Democratizing ICT for Development with DIY Innovation and Open Data

The recent Net Impact conference in Portland proved to be an ideal space to take a few steps back and reflect on the bigger picture. There was much talk of new and alternative approaches to traditional development. The word “participatory” in particular was a trending topic among both presenters and participants. But exactly how “participatory” are these “participatory” approaches to develop-ment? Do they fundamentally democratize the development process? And do these “novel” participatory approaches really let go of control? Should they? The following thoughts and ideas were co-developed in follow-up conversations with my colleague Chrissy Martin who also attended Net Impact. She blogs at Innovate.Inclusively.

I haven’t had the space recently to think through some of these questions or reflect about how the work I’ve been doing with Ushahidi fits (or doesn’t) within the traditional development paradigm—a paradigm which many at the confer-ence characterized as #fail. Some think that perhaps technology can help change this paradigm, hence the burst of energy around the ICT for Development (ICT4D) field. That said, it is worth remembering that the motivations driving this shift are more important than any one technology. For example, recall the principles behind the genesis of the Ushahidi platform: Democratizing information flows and access; promoting Open Data and Do it Yourself (DIY) Innovation with free, highly hackable (i.e., open source) technology; letting go of control.

The Ushahidi platform is not finished. It will never be finished. This is deliberate, not an error in the code. Free and open source software (FOSS) is by definition in a continual phase of co-Research and Development (co-R&D). The Ushahidi platform is not a solution, it is a platform on top of which others build their own solutions. These solutions remain open source and some are folded back into the core Ushahidi code. This type of “open protocol” can reverse “innovation cascades” leading to “reverse innovation” from developing to indus-trialized countries (c.f. information cascades). FOSS acts like a virus, it self-propagates. The Ushahidi platform, for example, has propagated to over 130 countries since it was first launched during Kenya’s post-election violence almost four years ago.

In some ways, the Ushahidi platform can be likened to a “choose your own adventure” game. The readers, not the authors, finish the story. They are the main characters who bring the role playing games and stories to life. But FOSS goes beyond this analogy. The readers can become the authors and vice versa. Welcome to co-creation. Perhaps one insightful analogy is the comparison between Zipcar and RelayRides.

I’ve used the Zipcar for over five years now and love it. But what would a “democratized” Zipcar look like? You guessed it: RelayRides turns every car owner into their own mini-DIY-Zipcar company. You basically get your own “Zipcar-in-a-box” kit and rent out your own car in the same way that Zipcar does with their cars. RelayRides is basically an open source version of Zipcar, a do-it-yourself innovation. A good friend of mine, Becca, is an avid RelayRides user. The income from lending her car out lets her cover part of her rent, and if she needs a car while hers is rented out, she’ll get online and look for available RelayRides in her neighborhood. She likes the “communal ownership” spirit that the technology facilitates. Indeed, she is getting to know her neighbors better as a result. In this case, DIY Innovation is turning strangers, a crowd, into a comm-unity. Perhaps DIY Innovation can facilitate community building in the long run.

The Ushahidi platform shares this same spirit. The motivation behind Ushahidi’s new “Check-In’s” feature, for example, is to democratize platforms like Foursquare. There’s no reason why others can’t have their own Foursquares and customize them for their own projects along with the badges, etc. That’s not to imply that the Ushahidi platform is perfect. There’s a long way to go, but again, it will never be perfect nor is that the intention. Sure, the technology will become more robust, stable and extensible, but not perfect. Perfection denotes an endstate. There is no endstate in co-R&D. The choose your own adventure story continues for as long as the reader, the main character decides to read on.

I’m all for “participatory development” but I’m also interested in allowing indivi-duals to innovate for themselves first and then decide how and who to participate with. I’d call that self-determination. This explains why the Ushahidi team is no longer the only “game in town” so-to-speak. Our colleagues at DISC have customized the Ushahidi platform in more innovative and relevant ways than we could have for the Egyptian context. Not only that, they’re making a business out of customizing the platform and training others in the Arab World. The Ushahidi code is out of our hands and it has been since 2008. We’re actively promoting and supporting partners like DISC. Some may say we’re nurturing our own competition. Well then, even better.

Freely providing the hackable building blocks for DIY Innovation is one way to let go of control and democratize ICT4D. Another complementary way is to democratize information access by promoting automated Open Data generation, i.e., embedded real-time sensors for monitoring purposes. Equal and public access to Open Data levels the playing field, prevents information arbitrage and disrupts otherwise entrenched flows of information. Participatory development without Open Data is unlikely to hold institutions accountable or render the quality of their services (or lack thereof) more transparent. But by Open Data here I don’t only mean data generated via participatory surveys or crowdsourcing.

The type of public-access Open Data generation I’m interested in could be called “Does-It-Itself” Open Data, or DII Data. Take “The Internet of Things” idea and apply this to traditional development. Let non-intrusive, embedded and real-time sensors provide direct, empirical and open data on the status of develop-ment projects without any “middle man” who may have an interest in skewing the data. In other words, hack the Monitoring and Evaluation process (M&E) by letting the sensors vote for themselves and display the “election results” publicly and in real time. Give the sensors a voice. Meet Evan Thomas, a young professor at Portland State, who spends his time doing just this at SweetLab, and my colleague Rose Goslinga who is taking the idea of DII Data to farmers in Kenya.

Evan embeds customized sensors to monitor dozens of development projects in several countries. These sensors generate real-time, high-resolution data that is otherwise challenging, expensive and time-consuming to collect via the tradi-tional survey-based approach. Evan’s embedded sensors generate behavior and usage data for projects like the Mercy Corps Water and Sanitation Program and Bridges to Prosperity Program. Another example of DII Data is Rose’s weather index insurance (WII) project in Kenya called Kilimo Salama. This initiative uses atmospheric data automatically transmitted via local weather towers to determine insurance payouts for participating farmers during periods of drought or floods. Now, instead of expensive visits to farms and subjective assessments, this data-driven approach to feedback loops lowers program costs and renders the process more objective and transparent.

There is of course more to the development field than the innovative processes described above. Development means a great many things to different people. The same is true of the words “Democracy”, “Participatory” and “Crowd-sourcing.” For me, crowdsourcing, like democracy, is a methodology that can catalyze greater participation and civic engagement. Some liken this to demo-cratizing the political process. Elections, in a way, are crowdsourced. Obviously, however, crowdsourced elections in no way imply that they are free, open or fair. Moreover, elections are but one of the ingredients in the recipe for  a democratic, political process.

In the same way, democratizing ICT4D is not a sufficient condition to ensure that the traditional development space obtains a new hashtag: #success. Letting go of control and allowing for self-determination can of course lead to unexpected outcomes. At this point, however, given the #fail hashtag associated with traditional development, perhaps unexpected outcomes driven by democratic, bottom-up innovation processes that facilitate self-organization, determination and participation, are more respectful to human dignity and ingenuity.

Communication and Human Development

The Berkman Center at Harvard University hosted a fascinating panel discussion figuring Amartya Sen, Michael Spence, Yochai Benkler and Clotilde Fonseca. The panelists addressed the role of communication and ICTs in human development, growth and poverty reduction.  They discussed what has changed, been learned, not been learned, needs to be learned, needs to be done most urgently.

Source: Berkman Center
Source: Berkman Center

Some brief notes and take-away’s:

  • Amatya Sen compared access to information via mobile phone to nutrition. Just like better nutrition may have adverse effects such as domestic violence, so does the mobile phone vis-a-vis the expansion of freedom. But this doesn’t mean we should abandon nutrition projects. Sen cautions against setting dichotomous priorities, e.g., development first or democracy first.
  • Michael Spence explained that the mobile phone as an important input in the production function of an economy. One principal concern resulting from the incredible growth in the mobile phone network is that regulators may react strongly to regulate this growth. Spencer adds that there is no silver bullet in development.
  • Clotilde Fonseca noted that the mobile phone is not yet a powerful device in the developing world; the distinction between voice and data is key. Most mobile phones in the developing world do not carry a high through-put of data. Clotilde cautions against applying a linear view of development to the ICT4D field. She adds that the digital divide is also a cognitive divide. There is also a capacity divide, i.e., the ability to absorb information.
  • Yochai Benkler remarked that the mobile phone tends towards more decentralized communication. That said, the question is more decentralized relative to what? Benkler also notes that not all solutions here have to be mobile. He also foresees many new opportunities for entrepreneurship in decentralized technology ecosystem ripe with tools, training and services.

For a very good, more detailed summary, please see my colleague Kate Brodock’s blog post here.

Patrick Philippe Meier