VIGIL: An Extensible System for Real-Time Detection and Mitigation of Cognitive Bias Triggers

TL;DR

VIGIL is a browser extension designed to detect and mitigate cognitive bias triggers in real-time online, using LLMs and extensible plugins, to improve information integrity and civic discourse.

Contribution

It introduces the first real-time, browser-based system for detecting and mitigating cognitive bias triggers with extensibility and privacy considerations.

Findings

Includes several validated plugins for NLP benchmarks.

Provides in-situ, scroll-synced detection and reversible reformulation.

Open-sourced at https://github.com/aida-ugent/vigil.

Abstract

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.