Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning

Journal
International Workshop & Tutorial on Interactive Adaptive Learning (IAL)
ISSN
1613-0073
ISSN-Digital
1613-0073
Type
conference paper
Date Issued
2023-09-22
Author(s)
Marek Herde
;
Denis Huseljic
;
Bernhard Sick
;
Ulrich Bretschneider
;
Sarah Oeste-Reiss
Research Team
IWI6
Abstract
Crowdworking is a popular approach for annotating large amounts of data to train deep neural networks. However, parts of the annotations are often erroneous. In a case study, we demonstrate how an intelligent crowdworker selection via deep learning reduces the number of erroneous annotations and, thus, the annotation costs of obtaining reliable data for training deep neural networks.
Language
English
Keywords
Crowdwork
Principal Agent Theorem
Adverse Selection
Moral Hazard
Smart Contract
HSG Classification
contribution to scientific community
Refereed
Yes
Event Title
International Workshop & Tutorial on Interactive Adaptive Learning (IAL)
Event Location
Torino, Italy
Event Date
22 Sep 2023
Official URL
URL
Subject(s)

information managemen...

Division(s)

IWI - Institute of In...

File(s)
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open.access

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JML_950.pdf

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Main Article

Description
Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning
Size

514.94 KB

Format

Adobe PDF

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