Measuring Bias in German Prompts to GPT Models Using Contact Hypothesis

Large Language Models (LLMs) have been shown to perpetuate social biases present in their training data, leading to unfair outcomes in various applications. Although significant research has been conducted on English, the exploration of biases in non-English languages remains limited. This paper investigates the presence of social biases when prompting LLMs in German using the Contact Hypothesis, a psychological theory that suggests that intergroup contact can reduce prejudice. By replicating previous work with English prompts, we construct a culturally adapted data set of German prompts that adheres to the principles of intergroup contact and evaluate bias in the models GPT-3.5, GPT-4 and GPT-4o. Our findings reveal that bias patterns when prompting LLMs in German differ from their English counterparts, with higher bias levels in German outputs, particularly under negative contact conditions. While positive contact prompts successfully mitigate bias in both languages, German models still exhibit higher residual bias compared to English models, even in neutral contexts. Additionally, our study highlights the importance of culturally relevant prompt design, as direct translations from English might fail to account for linguistic and societal differences in bias expression. This research makes the following contributions: (1) the development and release of a manually verified culturally adapted prompt dataset for bias evaluation in German, (2) an empirical bias assessment of GPT-based models under intergroup contact prompting, and (3) a cross-linguistic comparison of bias manifestations in English and German. Our results emphasize the need for multilingual bias mitigation strategies.