An Extendable Toolkit for Managing Quality of Human-Based Electronic Services

Micro-task markets like Amazon MTurk enable online workers to provide human intelligence as Web-based on demand services (so called "people services"). Businesses facing large amounts of knowledge work can benefit from increased flexibility and scalability of their workforce but need to cope with reduced control of result quality. While this problem is well recognized, it has so far only rudimentarily been addressed by existing platforms and tools. In this paper, we present a flexible research toolkit which enables experiments with advanced quality management mechanisms for generic micro-task markets. The toolkit enables control of correctness and performance of task fulfillment by means of continuous sampling, dynamic majority voting and worker pooling. While we demonstrate its application and performance for an OCR scenario building on Amazon MTurk, the toolkit supports the development of advanced quality management mechanisms for a large variety of people service scenarios and platforms.