Practice of Crowdsourcing

Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.

Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.