利用AI偏见设计工具，提升读者辨别虚假新闻的批判性思维能力
Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers
- 基于用户政治立场个性化推送内容，结合确认偏误与认知失调心理机制
- 通过渐进式引入多元观点，显著提升用户对信息的反思意识
- 适合关注媒体素养、人机交互与社会影响的研究者和产品设计者
本文探讨了利用大语言模型（LLMs）设计传播误导信息检测工具的可行性。鉴于人工智能模型在政治语境中固有的偏见，研究提出将这些偏见转化为促进批判性思维的工具。不同于通常视偏见为负面因素的观点，本研究结合确认偏误与认知失调的心理学概念，探索基于用户政治立场的个性化选择策略。通过一项定性用户研究，得出关键设计建议：提升偏见意识、支持个性化与选择权，并逐步引入多元视角。研究为构建具有社会责任感的AI辅助信息判断系统提供了实证依据与实践方向。
原文摘要 · Abstract (English)
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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