Crowd-Machine Collaboration for Item Screening
This addresses a recurring problem in many domains for applications requiring item screening, but it is incremental as it builds on existing hybrid approaches.
The paper tackles the problem of efficiently screening items that satisfy a set of predicates by combining crowd and machine classifiers, presenting hybrid algorithms that estimate gains in performance and cost over human-only or machine-only screening.
In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates. We show that this is a recurring problem in many domains, present machine-human (hybrid) algorithms that screen items efficiently and estimate the gain over human-only or machine-only screening in terms of performance and cost.