Collaboratively crowdsourcing workflows with turkomatic
Author(s) -
Anand Kulkarni,
Matthew Can,
Björn Hartmann
Publication year - 2012
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2145204.2145354
Subject(s) - crowdsourcing , workflow , computer science , process (computing) , plan (archaeology) , crowds , work (physics) , human–computer interaction , knowledge management , data science , world wide web , database , computer security , engineering , archaeology , operating system , mechanical engineering , history
Preparing complex jobs for crowdsourcing marketplaces requires careful attention to workflow design, the process of decomposing jobs into multiple tasks, which are solved by multiple workers. Can the crowd help design such workflows? This paper presents Turkomatic, a tool that recruits crowd workers to aid requesters in planning and solving complex jobs. While workers decompose and solve tasks, requesters can view the status of worker-designed workflows in real time; intervene to change tasks and solutions; and request new solutions to subtasks from the crowd. These features lower the threshold for crowd employers to request complex work. During two evaluations, we found that allowing the crowd to plan without requester supervision is partially successful, but that requester intervention during workflow planning and execution improves quality substantially. We argue that Turkomatic's collaborative approach can be more successful than the conventional workflow design process and discuss implications for the design of collaborative crowd planning systems.
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