Biased by Design: Leveraging Inherent AI Biases to Enhance Critical Thinking of News Readers
This addresses the issue of misinformation for news consumers by offering a novel approach to AI tool design, though it appears incremental in its application of existing psychological theories.
This paper tackles the problem of propaganda detection by leveraging inherent AI biases in LLMs to enhance critical thinking in news readers, proposing user choice and personalization strategies based on psychological concepts and presenting qualitative findings from a user study.
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.