Large language models, including versions like ChatGPT-5 mini and Llama 3.1, have been found to exhibit significant bias against speakers of various dialects, attributing negative stereotypes to them, according to a study conducted by researchers from Germany and the United States. The findings, reported by DW, indicate that these models often characterize speakers of German dialects such as Bavarian and Cologne as “uneducated,” “farm workers,” and “prone to anger.” The bias was found to intensify when the AI was specifically prompted about the dialect in question.
Minh Duc Bui, one of the lead authors of the study from Johannes Gutenberg University, commented, “I believe we see truly shocking epithets attributed to dialect speakers.” This research raises concerns over the implications of such biases, especially as AI technologies become increasingly integrated into daily life.
Similar biases have been noted in studies beyond German dialects. A 2024 analysis from the University of California, Berkeley, compared responses from ChatGPT to dialects of English, including Indian, Irish, and Nigerian. The results showed that the chatbot’s responses contained more pronounced stereotypes and derogatory language when interacting with dialect speakers compared to standard American or British English. Emma Harvey, a computer science graduate student at Cornell University, described the bias against dialects as “significant and troubling.”
In a separate instance during the summer of 2025, Harvey and her colleagues discovered that Amazon’s shopping assistant, Rufus, provided vague or incorrect responses to queries written in African American English, with rudeness escalating when the input contained errors. Another alarming example involved an Indian job applicant who used ChatGPT to review his resume; the chatbot altered his surname to one typically associated with a higher caste.
“The widespread adoption of language models threatens not just to preserve entrenched prejudices but to amplify them on a large scale,” Harvey warned. “Instead of mitigating harm, technologies risk giving it a systemic character.”
The issues surrounding bias are compounded by the fact that some AI models struggle to even recognize dialects. In July, for example, the AI assistant of Derby City Council in England failed to understand a radio host when she used local dialect terms such as “mardy” (meaning “whiner”) and “duck” (meaning “dear”).
The study’s findings point to a systemic problem rooted in how AI models are trained. These chatbots are fed vast amounts of text from the internet, which can often contain biases against specific dialect speakers. Carolin Holtermann from the University of Hamburg explained, “The main question is who writes this text. If it contains biases against dialect speakers, the AI will replicate them.”
Despite these challenges, Holtermann remained optimistic about potential solutions. She noted that unlike humans, biases within AI systems can be identified and “switched off,” allowing for targeted efforts to combat these issues. Some researchers advocate for the creation of customized models for specific dialects. A notable example is Acree AI’s introduction of the Arcee-Meraj model in August 2024, which is designed to work with multiple Arabic dialects.
Holtermann emphasized that the development of new and adaptable large language models (LLMs) could shift perceptions of AI. She stated that we should regard AI “not as an enemy of dialects but as an imperfect tool that can be improved.” As AI technology continues to evolve, addressing these biases becomes increasingly vital, not only for ethical considerations but also for enhancing the functionality of AI in diverse linguistic contexts.
In light of these findings, the conversation surrounding AI’s impact on language and culture is expected to intensify. As AI technologies proliferate, stakeholders must consider the implications of entrenched biases and work collaboratively to create systems that are both fair and effective.
See also
Researchers Expose Fault Injection Vulnerabilities in ML-Based Quantum Error Correction
AI Reveals Two New Subtypes of Multiple Sclerosis, Transforming Treatment Approaches
Gujarat Launches IAIRO AI Research Body with Rs 300 Crore PPP Model at GIFT City
AI-Based Digital Pathology Market to Reach $2.32 Billion by 2035, Growing at 8.7% CAGR
I/O Fund Reveals Top Free AI Stock Insights for 2025 with 210% Cumulative Returns
