by Ustalov, Dmitry, Savage, Saiph, van Berkel, Niels and Liu, Yang
Abstract:
Crowdsourcing 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.
Reference:
D. Ustalov, S. Savage, N. van Berkel, Y. Liu, "4th Crowd Science Workshop — CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling", in Adjunct Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM'23 Workshop), 2023, 1.
Bibtex Entry:
@inproceedings{Ustalov2023Candle,
title = {4th Crowd Science Workshop — CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling},
author = {Ustalov, Dmitry and Savage, Saiph and van Berkel, Niels and Liu, Yang},
year = 2023,
booktitle = {Adjunct Proceedings of the ACM International Conference on Web Search and Data Mining},
location = {WSDM'23 Workshop},
pages = 1,
doi = {10.1145/3539597.3572703},
url = {https://nielsvanberkel.com/files/publications/wsdm2023a.pdf},
abstract = {Crowdsourcing 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.},
type = {Conference Paper},
}