Adaptive Crowdsourcing Via Self-Supervised Learning
This work addresses the challenge of efficiently aggregating noisy human judgments in crowdsourcing systems, offering a scalable alternative to computationally intensive methods like expectation maximization.
The paper tackles the problem of improving group estimates in crowdsourcing by introducing a self-supervised learning method that adaptively weights workers based on past performance, achieving more accurate results than simple averaging when worker skills vary or estimates are correlated.
Common crowdsourcing systems average estimates of a latent quantity of interest provided by many crowdworkers to produce a group estimate. We develop a new approach -- predict-each-worker -- that leverages self-supervised learning and a novel aggregation scheme. This approach adapts weights assigned to crowdworkers based on estimates they provided for previous quantities. When skills vary across crowdworkers or their estimates correlate, the weighted sum offers a more accurate group estimate than the average. Existing algorithms such as expectation maximization can, at least in principle, produce similarly accurate group estimates. However, their computational requirements become onerous when complex models, such as neural networks, are required to express relationships among crowdworkers. Predict-each-worker accommodates such complexity as well as many other practical challenges. We analyze the efficacy of predict-each-worker through theoretical and computational studies. Among other things, we establish asymptotic optimality as the number of engagements per crowdworker grows.