系统梳理自然语言中刻板印象检测的研究现状与挑战
A Survey on Stereotype Detection in Natural Language Processing
- 从心理学等多学科定义出发，构建刻板印象检测的理论框架
- 筛选6000+篇论文，发现多语言、交叉性是未来关键方向
- 为预防歧视升级提供早期监测思路，适合伦理与AI安全研究者
刻板印象影响社会认知，可能引发歧视与暴力。尽管自然语言处理在性别偏见和仇恨言论方面已有广泛研究，但刻板印象检测仍是新兴领域，具有重要社会意义。本文综述现有研究，结合心理学、社会学和哲学中的定义，通过Semantic Scholar进行半自动文献调研，筛选2000-2025年间超过6000篇论文，识别出主要趋势、方法、挑战与未来方向。研究强调刻板印象检测可作为防止偏见恶化和仇恨言论上升的早期监测工具，呼吁未来研究采用更广泛的多语言与交叉性视角。
原文摘要 · Abstract (English)
Stereotypes influence social perceptions and can escalate into discrimination and violence. While NLP research has extensively addressed gender bias and hate speech, stereotype detection remains an emerging field with significant societal implications. In this work is presented a survey of existing research, analyzing definitions from psychology, sociology, and philosophy. A semi-automatic literature review was performed by using Semantic Scholar. We retrieved and filtered over 6,000 papers (in the year range 2000-2025), identifying key trends, methodologies, challenges and future directions. The findings emphasize stereotype detection as a potential early-monitoring tool to prevent bias escalation and the rise of hate speech. Conclusions highlight the need for a broader, multilingual, and intersectional approach in NLP studies.
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