The near real-time crisis mapping of the disasters in Haiti and Chile using Ushahidi required a substantial number of student volunteers. These volunteers were not the proverbial crowd but rather members of pre-existing, highly-connected social networks: universities. How do we move from netsourcing to crowdsourcing and on to turksourcing?
Student volunteers from Fletcher/Tufts, SIPA/Columbia and the Graduate Institute in Geneva all represent established social networks and not an anonymous crowd. They contributed over ten thousand free hours over the past 3 months to monitor hundreds of sources on the web and map actionable information on an ongoing basis. The Core Team at Fletcher spent dozens of hours training volunteers on media monitoring and mapping.
Netsourcing presents some important advantages. Pre-existing social ties can help mobilize a trusted volunteer network. I just sent one email to the Fletcher list-serve and because the Fletcher student body is a tight community, this eventually let do hundreds of volunteers being trained and contributing to the crisis mapping of Haiti.
At the same time, however, netsourcing is bounded crowdsourcing. In other words, netsourcing is scale-constrained. Imagine if Wikipedia contributions had been limited to professors only—that too would be bounded crowdsourcing. So how do we move from netsourcing to crowdsourcing crisis information? How do we move from having 300 volunteers connected via existing social networks to 300,000 or even 3,000,000 anonymous volunteers?
This was one of the many questions that my colleague Riley Crane, a friend of his and I discussed for almost 4 hours over dinner. (Riley recently rose to fame when he and his team at MIT that won DARPA’s Red Balloon competition). The answer, we think, is to develop a Mechanical Turk Service plug-in for Ushahidi. I’m calling this turksourcing. The two most time-consuming tasks that volunteers labored on was media monitoring and geo-location. Both processes can be disaggregated into human intelligence tasks (HITs) combined with some automation, like Swift River. And none of this would require prior training.
This is a conversation I very much look forward to continuing with Riley and one that I also plan to bring up at Nathan Eagle‘s Symposium on Artificial Intelligence for Development (AI-D) at Stanford next Monday. There is another related conversation that I’m excited to continue—namely the use of distributed, mobile gaming as an incentive catalytic for collective action, an area that Riley has spent a lot of time thinking about.
In terms of Ushahidi, If turksourcing crisis information can be combined with gaming, users could compete for altruism points, e.g., for how many HITs they contributed to a disaster response. This could be a proxy for how “good” a person is; a kind of public social ranking score for those who opt in. I imagine having individuals include their score and ranking on their blog (much like the number of Twitter followers they have). Who knows, a high altruism score could even get you more dates on Match.com.