Crowd-sourcing labor markets (e.g., Amazon Mechanical Turk) are booming, because they enable rapid construction of complex workflows that seamlessly mix human computation with computer automation. Example applications range from photo tagging to audio-visual transcription and interlingual translation. Similarly, workflows on citizen science sites (e.g. GalaxyZoo) have allowed ordinary people to pool their effort and make interesting discoveries. Unfortunately, constructing a good workflow is difficult, because the quality of the work performed by humans is highly variable. Typically, a task designer will experiment with several alternative workflows to accomplish a task, varying the amount of redundant labor, until she devises a control strategy that delivers acceptable performance.
Fortunately, this control challenge can often be formulated as an automated planning problem ripe for algorithms from the probabilistic planning and reinforcement learning literature. I describe our recent work on the decision-theoretic control of crowd sourcing and suggest open problems for future research. In particular, I discuss:
The use of partially-observable Markov decision Processes (POMDPs) to control voting on binary-choice questions and iterative improvement workflows.
Decision-theoretic methods that dynamically switch between alternative workflows in a way that improves on traditional (static) A-B testing.
A novel workflow for crowdsourcing the construction of a taxonomy — a challenging problem since it demands a global perspective of the input data when no one worker sees more than a tiny fraction.
Methods for optimizing the acquisition of labeled training data for use in machine learning applications; this an important special case, since data annotation is often crowd-sourced.