More for less: adaptive labeling payments in online labor markets

Tomer Geva, Maytal Saar-Tsechansky*, Harel Lustiger

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In many predictive tasks where human intelligence is needed to label training instances, online crowdsourcing markets have emerged as promising platforms for large-scale, cost-effective labeling. However, these platforms also introduce significant challenges that must be addressed in order for these opportunities to materialize. In particular, it has been shown that different trade-offs between payment offered to labelers and the quality of labeling arise at different times, possibly as a result of different market conditions and even the nature of the tasks themselves. Because the underlying mechanism giving rise to different trade-offs is not well understood, for any given labeling task and at any given time, it is not known which labeling payments to offer in the market so as to produce accurate models cost-effectively. Importantly, because in these markets the acquired labels are not always correct, determining the expected effect of labels acquired at any given payment on the improvement in model performance is particularly challenging. Effective and robust methods for dealing with these challenges are essential to enable a growing reliance on these promising and increasingly popular labor markets for large-scale labeling. In this paper, we first present this new problem of Adaptive Labeling Payment (ALP): how to learn and sequentially adapt the payment offered to crowd labelers before they undertake a labeling task, so as to produce a given predictive performance cost-effectively. We then develop an ALP approach and discuss the key challenges it aims to address so as to yield consistently good performance. We evaluate our approach extensively over a wide variety of market conditions. Our results demonstrate that the ALP method we propose yields significant cost savings and robust performance across different settings. As such, our ALP approach can be used as a benchmark for future mechanisms to determine cost-effective selection of labeling payments.

Original languageEnglish
Pages (from-to)1625-1673
Number of pages49
JournalData Mining and Knowledge Discovery
Issue number6
StatePublished - 1 Nov 2019


FundersFunder number
Henry Crown Center for Business Research
Jeremy Coller Foundation
Boston University
Temple University
Tel Aviv University


    • Adaptive labeling payments
    • Crowdsourcing
    • Label acquisition
    • Machine learning
    • Online labor markets
    • Supervised learning


    Dive into the research topics of 'More for less: adaptive labeling payments in online labor markets'. Together they form a unique fingerprint.

    Cite this