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Preventing profiling for ethical fake news detection.

Liesbeth Allein1,2, Marie-Francine Moens2, Domenico Perrotta1

  • 1European Commission, Joint Research Centre (JRC), Italy.

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Summary
This summary is machine-generated.

This study introduces a novel algorithm to detect fake news without profiling users, enhancing ethical AI. The method improves classification accuracy by integrating social context during optimization, not evaluation.

Keywords:
Constrained representation learningEthicsFake news detectionNatural language processingProfiling

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Area of Science:

  • Artificial Intelligence
  • Computational Social Science
  • Natural Language Processing

Background:

  • Online news audience data offers insights but risks profiling in fake news detection.
  • Ethical AI demands methods that avoid user profiling while leveraging social context.

Discussion:

  • A new profiling-avoiding algorithm uses Twitter user data for model optimization but excludes it during veracity evaluation.
  • Two novel objective functions maximize correlation between articles and their spreaders, and among spreaders themselves.
  • The algorithm was tested on three neural classifiers using diverse fake news datasets.

Key Insights:

  • The profiling-avoiding approach significantly improves prediction performance in fake news detection.
  • User-inspired classifiers demonstrate enhanced discrimination between fake and true news in latent spaces.
  • This research addresses the critical issue of profiling dependency in user-informed fake news detection.

Outlook:

  • This work provides a foundation for developing more ethical and effective AI-driven fake news detection systems.
  • Future research can explore broader social network integration and diverse datasets.
  • The findings encourage further investigation into balancing user data utility with privacy preservation in AI.