首个实时检测并缓解认知偏见诱因的浏览器插件，保护网络信息可信度。
VIGIL: An Extensible System for Real-Time Detection and Mitigation of Cognitive Bias Triggers
- 基于LLM实时识别内容中的认知偏见触发点
- 支持可逆的语义改写与离线/云端混合推理
- 开源可扩展，适合媒体素养研究者使用
生成式AI正日益威胁在线信息完整性和公共话语质量，尤其体现在虚假和错误信息传播上。尽管已有媒体素养与透明度工具用于评估信息真实性及来源可靠性，但更隐蔽却可能更严重的威胁是利用人类认知偏见和认知局限进行说服或操纵。据我们所知，目前尚无工具能直接检测并缓解在线信息中认知偏见触发因素的存在。本文提出VIGIL（VIrtual GuardIan angeL），首个支持实时认知偏见触发点检测与缓解的浏览器扩展，具备滚动同步检测、基于大模型的可逆改写能力，以及从完全离线到云端的隐私分级推理。VIGIL设计为可扩展架构，支持第三方插件，多个插件已在NLP基准上严格验证。项目已开源：https://github.com/aida-ugent/vigil。
原文摘要 · Abstract (English)
The rise of generative AI is posing increasing risks to online information integrity and civic discourse. Most concretely, such risks can materialise in the form of mis- and disinformation. As a mitigation, media-literacy and transparency tools have been developed to address factuality of information and the reliability and ideological leaning of information sources. However, a subtler but possibly no less harmful threat to civic discourse is to use of persuasion or manipulation by exploiting human cognitive biases and related cognitive limitations. To the best of our knowledge, no tools exist to directly detect and mitigate the presence of triggers of such cognitive biases in online information. We present VIGIL (VIrtual GuardIan angeL), the first browser extension for real-time cognitive bias trigger detection and mitigation, providing in-situ scroll-synced detection, LLM-powered reformulation with full reversibility, and privacy-tiered inference from fully offline to cloud. VIGIL is built to be extensible with third-party plugins, with several plugins that are rigorously validated against NLP benchmarks are already included. It is open-sourced at https://github.com/aida-ugent/vigil.
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