OpenAI's ChatGPT language model has gained popularity as a powerful tool for complex problem-solving and information retrieval. However, concerns arise about the reproduction of biases present in the language-specific training data. In this study, we address this issue in the context of the Israeli-Palestinian and Turkish-Kurdish conflicts. Using GPT-3.5, we employed an automated query procedure to inquire about casualties in specific airstrikes, in both Hebrew and Arabic for the former conflict and Turkish and Kurdish for the latter. Our analysis reveals that GPT-3.5 provides 27$\pm$11 percent lower fatality estimates when queried in the language of the attacker than in the language of the targeted group. Evasive answers denying the existence of such attacks further increase the discrepancy, creating a novel bias mechanism not present in regular search engines. This language bias has the potential to amplify existing media biases and contribute to information bubbles, ultimately reinforcing conflicts.

使用GPT-3.5模型，在以色列-巴勒斯坦冲突和土耳其-库尔德冲突的背景下，我们研究了ChatGPT语言模型存在的偏见问题。通过自动查询程序，我们分别使用希伯来语和阿拉伯语查询前一冲突中特定空袭的伤亡情况，以及土耳其语和库尔德语查询后一冲突的情况。分析结果显示，GPT-3.5模型在袭击者所用语言查询下，伤亡估计值相较于被攻击群体所用语言查询下要低27±11％。此外，回避性回答否认此类袭击的存在进一步增加了差异，形成了一种不同于常规搜索引擎的新的偏见机制。这种语言偏见有可能放大现有的媒体偏见，并对信息气泡产生贡献，最终加剧冲突。

聊天GPT中用户语言对冲突死亡估计的影响