Authors: Jeffrey Parsons, Shawn Ogunseye
Tags: 2016, classification, crowdsourcing, data quality, expertise
Subject matter expertise is widely believed to have a positive effect on information quality in crowdsourcing. Many data crowdsourcing systems are therefore designed to seek out contributions from experts in the crowd. We argue that expert contributors of data in crowdsourcing projects are proficient rule-based classifiers, and are efficient because they attend only to attributes of instances that are relevant to a classification task, while ignoring attributes irrelevant to the task at hand. We posit that this selective attention will negatively affect the tendency of experts to contribute data outside of categories anticipated in the design of a class-based data crowdsourcing platform. We propose hypotheses derived from this view, and outline two experiments to test them. We conclude by discussing the potential implications of this work for the design of crowdsourcing platforms and the recruitment of expert versus novice data contributors in studies of data quality in crowdsourcing settings.
Cite as:
Ogunseye S. and Parsons J. (2016). “The Downside of Expertise: Does Domain Knowledge have a Negative Effect on the Quality of Crowdsourced Data?,” in AIS SIGSAND, Lubbock, TX, United States, May 12-14, 2016.
