历史训练数据中的种族偏见对新闻AI的影响
Impacts of Racial Bias in Historical Training Data for News AI
AI总结：
本文研究了基于历史训练数据的新闻AI中种族偏见的影响，发现'blacks'标签在某些群体中作为种族歧视检测器，但对现代案例表现不佳，揭示了新闻室在采用AI工具时需平衡效率与偏见风险的挑战。
摘要：
人工智能技术已经迅速进入商业和研究应用，这些应用涉及大量文本语料库，包括计算新闻研究和新闻室环境。这些模型，训练于各种来源的现有数据，可以被概念化为历史文物，编码了几十年来的态度和刻板印象。本文研究了一个此类例子，该例子基于广泛使用的《纽约时报》注释语料库创建多标签分类器。我们在研究环境中使用时发现令人担忧的
英文摘要：
AI technologies have rapidly moved into business and research applications that involve large text corpora, including computational journalism research and newsroom settings. These models, trained on extant data from various sources, can be conceptualized as historical artifacts that encode decades-old attitudes and stereotypes. This paper investigates one such example trained on the broadly-used New York Times Annotated Corpus to create a multi-label classifier. Our use in research settings surfaced the concerning "blacks" thematic topic label. Through quantitative and qualitative means we investigate this label's use in the training corpus, what concepts it might be encoding in the trained classifier, and how those concepts impact our model use. Via the application of explainable AI methods, we find that the "blacks" label operates partially as a general "racism detector" across some minoritized groups. However, it performs poorly against expectations on modern examples such as COVID-19 era anti-Asian hate stories, and reporting on the Black Lives Matter movement. This case study of interrogating embedded biases in a model reveals how similar applications in newsroom settings can lead to unexpected outputs that could impact a wide variety of potential uses of any large language model-story discovery, audience targeting, summarization, etc. The fundamental tension this exposes for newsrooms is how to adopt AI-enabled workflow tools while reducing the risk of reproducing historical biases in news coverage.
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。
