Online labor marketplaces offer the potential to automate a variety of tasks too difficult for computers, but present requesters with significant difficulties in obtaining accurate results. We share experiences from building MobileWorks, a crowd platform that departs from the marketplace model to provide robust, high-quality results. Three architectural contributions yield measurably improved accuracy on input tasks.  A dynamic work routing system identifies expertise in the crowd and ensures that all work posted into the system is completed with bounded completion times and at fair worker prices. A peer management system ensures that incorrect answers are prevented by experienced members of the crowd. Last, social interaction techniques give the best workers the ability and incentives to manage, teach & supervise other members of the crowd, as well as to clarify tasks. This process filters worker error and allows the crowd to collectively learn how to solve unfamiliar tasks. (extended abstract)

Existing crowdsourcing markets are often inaccessible to workers living at the bottom of the economic pyramid. We present MobileWorks, a mobile phone-based crowdsourcing platform intended to provide employment to developing world users. MobileWorks provides human optical character recognition (OCR) tasks that can be completed by workers on low-end mobile phones through a web browser. To address the limited screen resolution available on low-end phones, MobileWorks divides documents into many small pieces and sends each piece to a different worker. An initial pilot study with 10 users over a two month period revealed that it is feasible to do basic OCR tasks using a simple mobile web-based application. We find that workers using MobileWorks average 120 tasks per hour at an accuracy rate of 99% using a multiple entry solution. In addition, users had a positive experience with MobileWorks: all study participants would recommend MobileWorks to friends and family.