Is Quality Control Pointless?

Intrinsic to the transition towards, and necessary for the success of digital platforms as a service (at scale) is the notion of human computation. Going beyond ‘the wisdom of the crowd’, human computation is the engine that powers platforms and services that are now ubiquitous like Duolingo and Wikipedia. In spite of increasing research and population interest, several issues remain open and in debate on large-scale human computation projects. Quality control is first among these discussions. We conducted an experiment with three different tasks of varying complexity and five different methods to distinguish and protect against constantly under-performing contributors. We illustrate that minimal quality control is enough to repel constantly under-performing contributors and that this effect is constant across tasks of varying complexity

Comparing human and algorithm performance on estimating word-based semantic similarity

Understanding natural language is an inherently complex task for computer algorithms. Crowdsourcing natural language tasks such as semantic similarity is therefore a promising approach. In this paper, we investigate the performance of crowdworkers and compare them to offline contributors as well as to state of the art algorithms. We will illustrate that algorithms do outperform single human contributors but still cannot compete with results gathered from groups of contributors. Furthermore, we will demonstrate that this effect is persistent across different contributor populations. Finally, we give guidelines for easing the challenge of collecting word based semantic similarity data from human contributors