StereoDetect: 通过社会心理基础正确检测刻板印象和反刻板印象
StereoDetect: Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings
AI总结：
StereoDetect通过社会心理学基础提出刻板印象和反刻板印象检测的五元组定义，构建了定义一致的基准数据集，揭示现有方法的不足并验证其有效性。
摘要：
刻板印象已知具有非常有害的影响，因此其检测至关重要。然而，当前研究主要集中在检测和评估刻板印象偏见，从而使得刻板印象的研究仍处于初级阶段。我们的研究发现，许多工作未能清晰地区分刻板印象和刻板印象偏见，这显著阻碍了该领域研究的进展。刻板印象和反刻板印象检测是一个需要社会知识的问题；因此，它是负责任的人工智能中最困难的领域之一。本文研究了这一任务，我们提出了一种五元组定义，并提供精确的术语来区分刻板印象、反刻板印象、刻板印象偏见和一般偏见。我们提供了一个基于社会心理学的概念框架，以实现可靠的检测。我们识别了现有基准在刻板印象和反刻板印象检测任务中的关键不足。为了解决这些差距，我们开发了StereoDetect，一个经过精心挑选、定义一致的基准数据集，用于此任务。我们展示了Sub-10B语言模型和GPT-4o经常错误分类反刻板印象，并且无法识别中性泛化。我们通过多种定性和定量比较，展示了StereoDetect与现有基准和在这些基准上微调的模型的成效。数据集和代码可在https://github.com/KaustubhShejole/StereoDetect上获得。
英文摘要：
Stereotypes are known to have very harmful effects, making their detection critically important. However, current research predominantly focuses on detecting and evaluating stereotypical biases, thereby leaving the study of stereotypes in its early stages. Our study revealed that many works have failed to clearly distinguish between stereotypes and stereotypical biases, which has significantly slowed progress in advancing research in this area. Stereotype and Anti-stereotype detection is a problem that requires social knowledge; hence, it is one of the most difficult areas in Responsible AI. This work investigates this task, where we propose a five-tuple definition and provide precise terminologies disentangling stereotypes, anti-stereotypes, stereotypical bias, and general bias. We provide a conceptual framework grounded in social psychology for reliable detection. We identify key shortcomings in existing benchmarks for this task of stereotype and anti-stereotype detection. To address these gaps, we developed StereoDetect, a well curated, definition-aligned benchmark dataset designed for this task. We show that sub-10B language models and GPT-4o frequently misclassify anti-stereotypes and fail to recognize neutral overgeneralizations. We demonstrate StereoDetect's effectiveness through multiple qualitative and quantitative comparisons with existing benchmarks and models fine-tuned on them. The dataset and code is available at https://github.com/KaustubhShejole/StereoDetect.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
