This is a series of reflections about a new area of research that’s rapidly gaining traction within Computer Science. It has various names (a sign of its youth), but many call it ICT4D. A quick web surf finds the following class websites. At CMU you can take Human Computer Interaction in the Developing World, at Washington and Berkeley you can take classes for Computing or Information and Communications Technologies for the Developing World. At Stanford you can take a class on Technologies for Liberation, which is part of a broader program on Liberation Technology, which takes a different perspective but clearly has relationship to the focus on emerging nations where many of these problems are particularly acute. And here at Georgia Tech we offer multiple classes in this space, including Computing4Good and Computers, Communications and International Development.
Classes are not just a necessity but also a reflection of topics that faculty think are important or interesting to teach. So, from this, and other evidence such as the growth in HCI4D work at the ACM CHI conference, and the NDSR workshop held at SOSP and previously at SIGCOMM conferences.
I’m interested in ICT4D, well something a bit different to that, for two reasons.
First, I supervise students who have contributions to make to the emerging body of scholarship in this area. One student, Susan Wyche, has used multi-sited ethnography to understand how ICTs are used in Kenya, Brazil and the United States. Another student, Andrea Grimes, is interested in ICTs for underserved communities within the U.S. Both do work that pushes on the definition of ICT4D. Traditionally, by virtue of being in the U.S., Andrea’s work would not be part of ICT4D. And yet, questions of design, implementation, and evaluation in under resourced communities cuts across her work in ways that are not dissimilar to discussions within ICT4D. What makes this all the more interesting is that Susan’s work which from it’s multi-sited nature is very central to ICT4D also pushes on definitions, by showing how somewhat resourced communities already appropriate ICTs, thus pushing on the idea that ICT4D is not what we will do but also what is already happening.
Second, I’m interested in ICT4D because it affords an opportunity to look at the discipline of Computer Science. I’m very interested in the formation of disciplines, how Science is a human-organized process, and how its organization affects what we do. ICT4D is going through some struggles with identity and legitimacy, and the questions that it raises give us a rare opportunity to examine the assumptions implicit in the discipline that it is trying to find its home within.
Some participants, i.e. those who come from CS orientations, struggle to answer the question “where’s the Computer Science in ICT4D?” And others list numerous opportunities (to empirically show what the potential might be for areas that span the fields of Computer Science, such as low-cost connectivity, getting content into developing regions via novel networking architectures and caching systems, mobile and low-OS footprint applications, power management, computer vision for detection problems in health).
But others have observed that the question is also an opportunity to inspect the assumptions that underlie the production of knowledge within Computer Science. Some people observe the following. First, that CS has been focused on problems that are experienced by those solving them. Second, that in publication, and the review that leads to that, Computer Scientists prioritize the solution to the problem, rather than the problem itself. And these are related. Clearly, if you pick problems you have first hand experience with then the balance time spent on problem discovery versus solution would likely emphasize the solution. ICT4D problems are not those that many (but not all) in Computer Science have spent time experiencing, so problem discovery and exploration take considerably more time. Some observe that HCI has done a good job of making problem explication a part of the science, but also note the difficulty that HCI continues to have in establishing its legitimacy in Computer Science.
Another argument that I’ve seen is that Computer Science tends to prioritize the complex technological solution over the simpler technological solution. One manifestation of this is to value high-end over low-end. This made me reflect on various research programs within Computer Science, including the relatively new “many-core” area. Many-core, like peta-scale and high-performance computing, emphasize in their very titles the high-endness of the technology platforms that are at once both problem and solution. I’ve wondered whether when many-core is not enough, whether we’ll move to an almost Seussian “many-more many-core” agenda.
And my point here is that it seems quite “natural” within Computer Science to organize an agenda around an abundance of complex technologies. ICT4D may have an abundance of cellphones, particularly low-end cellphones, but even that’s not always the case. The absence of complex technology makes the agenda harder to express. This is compounded by the fact that many other areas of Computer Science organize around machine components, databases, compilers, even networking, computer architecture, programming languages. Areas like Software Engineering and HCI are different, perhaps that also contributes to the difficulty that they sometimes have in being treated as legitimate areas of activity. Like Software Engineering and HCI, ICT4D as people note is not organized around abundance, it’s organized around a domain, and even that domain is contested and complicated.
ICD4D is truly interdisciplinary. It involves bringing people from multiple disciplines together. And the argument is made that the range of disciplines is bigger than HCI (also posited as an interdisciplinary field of Computer Science). But, I think it’s more than just in research process that interdisciplinary teams are needed, but also in the ways that solution success are measured. The objective of ICT4D is to solve hard research problems that simultaneously make a difference in the lives of people underserved by ICTs. We don’t measure CS by the good that it’s created for the middle class of America, we measure it by the complexity of the solution.
Actually, we do also measure the impact on the middle (and upper classes) of America, impact being the favoured keyword, when we talk about the innovations that Computer Science has provided and the ubiquity of those solutions in society (through, of course, largely corporate channels). So, our measures have been economic success for corporate America. But, does that seem like the right measure for ICT4D? Particularly since the business in a position to take advantage of ICT4D innovations is likely in the United States or another industrialized nation. But, even when we draw on this impact, people still conduct research on how we measure the impact of technologies.
ICT4D causes me, at least, to reflect on economic impact (which favors those who create successful start-ups since they are likely the only people who can easily draw a line between what they’ve done and how many people have purchased it or use it) as a metric for Computer Science’s impact. Additionally, given the difficulties of finding appropriate measures, I can’t help wondering whether ICT4D is being asked to put the cart before the horse, if we’re learning how to measure productivity gains for computer use in corporate America (who have had computers in place for decades) is it perhaps unrealistic to have well-understood metrics for settings where getting the computer in is going to be a significant first challenge?
Another assumption that comes to light when reading within the ICT4D literature concerns the place of abstraction in Computer Science. A solution that is generalisable, i.e. abstract enough that it works in all cases, is highly valued. In this way, Computer Science is perhaps no different from other sciences that seek fundamental principles. But, ICT4D is either a considerable distance from having those general solutions, or perhaps as some think is not a field of abstractions but of instances and understanding how instances differ as part of understanding what impact on people’s very different cultural, social, economic, geographical, political lives might be.
Finally, two other challenges for ICT4D. What impact means also turns on sustainability of the solution, it has to be something that works. Works in place, after the research team leave, by the people for whom it was designed. In traditional CS, if we do give our results to end-users (although frequently we let the marketplace do that for us) it is supported by a reliable power infrastructure, an educational infrastructure that gives many the knowledge to operate and manipulate the system, and so forth. So much less exists, and therefore so much more is required in ICT4D and by ICT4D practitioners. Also, these infrastructural absences appear to challenge our processes. HCI has many accounts of how participatory design failed because the people working with the researchers didnt understand why they didn’t know the answers, or that software was mallable enough to be the subject of redesign, or what the relationship between a paper prototype and the final system might be. Are we ready to have our methods turned over because actually they weren’t general enough? I think we should risk it, because what ICT4D will do is to expose the assumptions we make about access, wealth, market systems, education, power, etc… will all come to the forefront clearly.
So, I’d like to thank ICT4D for giving me an opportunity to look under the hood of Computer Science. As the area continues to grow, so these questions will be answered in some way, how is the open question right now.