4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling
4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling
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第四届群体科学研讨会 - CANDLE：人类协作和数据标记学习算法
DOI：
10.1145/3539597.3572703
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Liu, Yang
中科院分区：
文献类型：
--
作者：
Ustalov, Dmitry;Savage, Saiph;van Berkel, Niels;Liu, Yang
众包已被用于为机器学习和人工智能（AI）生产有影响力的大规模数据集，如ImageNET、SuperGLUE等。自21世纪初众包兴起以来，人工智能社区一直在从不同角度研究其计算、系统设计和以数据为中心的方面。我们欢迎关于开发和增强以众工为中心的工具的研究，这些工具提供任务匹配、请求者评估、指令验证等主题。我们也有兴趣探索利用众工的集成来提高机器学习模型的识别和性能的方法。因此，我们邀请专注于船舶主动学习技术的研究，从嘈杂数据和人群中联合学习的方法，人群与计算机交互的新方法，重复任务自动化以及人与机器之间的角色分离。此外，我们邀请设计和应用这些技术在各个领域的作品，包括电子商务和医学。
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.