Visual Bias Detection for Addressing Illegal Fishing Activities

In this work, we present a visual analytics approach designed to address the 2024 VAST Challenge Mini-Challenge 1, which focuses on detecting bias in a knowledge graph. Our solution utilizes pixel-based visualizations to explore patterns within the knowledge graph, CatchNet, which is employed to ide...

Full description

Saved in:
Bibliographic Details
Published in2024 IEEE Visual Analytics Science and Technology VAST Challenge pp. 9 - 10
Main Authors Buchmuller, Raphael, Furst, Daniel, Frings, Alexander, Schlegel, Udo, Keim, Daniel
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.10.2024
Subjects
Companies
Data aggregation
Data visualization
Knowledge graphs
Large language models
LLM
Mini-Challenge 1
Pipelines
Pixel Visualization
Sentiment analysis
VAST Challenge
Visual analytics
Online AccessGet full text

Cover

More Information
Summary:In this work, we present a visual analytics approach designed to address the 2024 VAST Challenge Mini-Challenge 1, which focuses on detecting bias in a knowledge graph. Our solution utilizes pixel-based visualizations to explore patterns within the knowledge graph, CatchNet, which is employed to identify potential illegal fishing activities. CatchNet is constructed by FishEye analysts who aggregate open-source data, including news articles and public reports. They have recently begun incorporating knowledge extracted from these sources using advanced language models. Our method combines pixel-based visualizations with ordering techniques and sentiment analysis to uncover hidden patterns in both the news articles and the knowledge graph. Notably, our analysis reveals that news articles covering critiques and convictions of companies are subject to elevated levels of bias.
DOI:10.1109/VASTChallenge64683.2024.00009