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Title: 4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling
rowdsourcing has been used to produce impactful and large-scale datasets for Machine Learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in early 2000s, the AI community has been studying its computational, system design, and data-centric aspects at various angles. We welcome the studies on developing and enhancing of crowdworker-centric tools, that offer task matching, requester assessment, instruction validation, among other topics. We are also interested in exploring methods that leverage the integration of crowdworkers to improve the recognition and performance of the machine learning models. Thus, we invite studies that focus on shipping active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines. Moreover, we invite works on designing and applying such techniques in various domains, including e-commerce and medicine.  more » « less
Award ID(s):
2203212
NSF-PAR ID:
10448622
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
WSDM: ACM International Web Seach and Data Mining Conference 2023.
Page Range / eLocation ID:
1268 to 1268
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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