Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912733360816128 |
|---|---|
| author | Zavolokina, Liudmila Sprenkamp, Kilian Katashinskaya, Zoya Jones, Daniel Gordon |
| author_facet | Zavolokina, Liudmila Sprenkamp, Kilian Katashinskaya, Zoya Jones, Daniel Gordon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers Zavolokina, Liudmila Sprenkamp, Kilian Katashinskaya, Zoya Jones, Daniel Gordon Human-Computer Interaction Artificial Intelligence 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. |
| title | Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2504.14522 |