This paper explores the design of a propaganda detection tool using Large Language Models (LLMs). Acknowledging the inherent biases in AI models, especially in political contexts, we investigate how these biases might be leveraged to enhance critical thinking in news consumption. Countering the typical view of AI biases as detrimental, our research proposes strategies of user choice and personalization in response to a user's political stance, applying psychological concepts of confirmation bias and cognitive dissonance. We present findings from a qualitative user study, offering insights and design recommendations (bias awareness, personalization and choice, and gradual introduction of diverse perspectives) for AI tools in propaganda detection.
View on arXiv@article{zavolokina2025_2504.14522,
title={ Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers },
author={ Liudmila Zavolokina and Kilian Sprenkamp and Zoya Katashinskaya and Daniel Gordon Jones },
journal={arXiv preprint arXiv:2504.14522},
year={ 2025 }
}