将人类智能和机器学习结合起来进行事实检查:迈向混合人在循环框架

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2023-10-27 DOI:10.3233/ia-230011
David La Barbera, Kevin Roitero, Stefano Mizzaro
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引用次数: 0

摘要

网络错误信息对现代社会构成了严重威胁。评估在线信息的真实性是一个复杂的问题,如今主要依靠训练有素的事实核查专家来解决。这种解决方案是不可扩展的,由于问题的重要性,该问题得到了科学界的重视,提出了许多基于人工智能和机器学习的方法。尽管作出了努力,但这些办法的效力还不足以使它们在没有监督的情况下使用。在这篇立场文件中,我们提出了一个混合的人在循环框架,用于事实核查:我们通过依赖于自动人工智能方法、众包方法和专家的组合来解决错误信息问题。我们研究了框架的单个组件以及它们之间的相互作用,我们提出了不同组件的交错,我们相信这将成为未来研究有效和可扩展的事实检查的有用起点。
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Combining human intelligence and machine learning for fact-checking: Towards a hybrid human-in-the-loop framework
Online misinformation is posing a serious threat for the modern society. Assessing the veracity of online information is a complex problem which nowadays is addressed by heavily relying on trained fact-checking experts. This solution is not scalable, and due to the importance of the problem the issue gained the attention of the scientific community, which proposed many based on Artificial Intelligence and Machine Learning methods. Despite the efforts made, the effectiveness of such approaches is not yet enough to allow them to be used without supervision. In this position paper, we propose a hybrid human-in-the-loop framework for fact-checking: we address the misinformation issue by relying on a combination of automatic Artificial Intelligence methods, crowdsourcing ones, and experts. We study the single components of the framework as well as their interactions, and we propose an interleaving of the different components which we believe will serve as a useful starting point for the future research towards effective and scalable fact-checking.
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
期刊最新文献
Special Issue NL4AI 2022: Workshop on natural language for artificial intelligence User-centric item characteristics for personalized multimedia systems: A systematic review Combining human intelligence and machine learning for fact-checking: Towards a hybrid human-in-the-loop framework A framework for safe decision making: A convex duality approach Grounding End-to-End Pre-trained architectures for Semantic Role Labeling in multiple languages
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