Paper Number
ECIS2025-1889
Paper Type
CRP
Abstract
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.
Recommended Citation
Zavolokina, Liudmila; Sprenkamp, Kilian; Katashinskaya, Zoya; and Jones, Daniel Gordon, "Biased by design: Leveraging Inherent AI Biases to Enhance Critical Thinking of News Readers" (2025). ECIS 2025 Proceedings. 8.
https://aisel.aisnet.org/ecis2025/hci/hci/8
Biased by design: Leveraging Inherent AI Biases to Enhance Critical Thinking of News Readers
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.
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