Anyone working with large language models (LLMs) quickly notices that the output is not always neutral. Sometimes a persistent stereotype emerges, a specific worldview is presented as absolute truth, or the model responds noticeably less accurately in Dutch than in English. This phenomenon is known as bias or distortion.
In this article, we dissect where these biases come from. We look at the role of training data, the impact of the language ratio, and the fundamental difference between bias in raw data, bias introduced during alignment, and bias you introduce yourself through your prompts. Without moralizing, but with a pragmatic, technical perspective and practical tools to detect and mitigate bias.
How does a language model learn bias?
To understand why a model can be biased, we must go back to the basics of how a neural network is trained. As explained in our article on how an AI learns, a language model absorbs patterns from massive amounts of text from the internet: books, articles, forums, and websites.
A language model has no consciousness of its own, no ethical compass, and no worldview. It is purely a statistical predictor that calculates the most likely next word based on previous texts. If the internet is full of texts in which certain roles, opinions, or cultural perspectives are disproportionately associated with each other, the model adopts that statistical correlation as a fundamental pattern.
Bias in a language model is essentially a reflection of patterns, omissions, and imbalances in the human creations with which the model has been fed.
The language barrier: why English dominates
A specific type of bias that directly affects Dutch users is the language gap. The vast majority of high-quality training data on the internet is in English. Scientific articles, programming code, technical documentation, and broad cultural analyses are primarily written in English.
Dutch is a relatively small language in the global data landscape. Although modern models can indeed communicate in Dutch, the amount of Dutch training data is many times smaller than the English data. This has direct consequences for the output:
- Less depth in specialized domains: In English, a model can draw from millions of pages on a niche topic. In Dutch, the context is sometimes so thin that the model has to fall back on translation-like generalizations or create hallucinations to fill the gap.
- Anglocentric cultural bias: Because the structure of much Dutch training data is influenced by translated English content or American culture, the model implicitly adopts American social relations, holidays, legislation, and reference frames as the 'standard'.
Three levels of bias
It is important to distinguish exactly where the bias arises. We can divide bias into three different categories within the lifecycle of a language model:
| Level | Cause | Example |
|---|---|---|
| 1. Data bias | Present in the raw collection of internet texts. | Historical data in which certain professions are exclusively attributed to a specific gender. |
| 2. Alignment bias | Introduced during RLHF (Reinforcement Learning from Human Feedback) or fine-tuning by human preferences. | A model that responds overly cautiously or with feigned politeness to sensitive topics due to strict safety filters. |
| 3. Prompt bias | Caused by the way the user formulates the prompt. | "Why do local entrepreneurs always fail in..." forces the model into a one-sided, confirmatory reasoning. |
How do you detect bias in your own application?
Recognizing bias in the output of an LLM requires a critical attitude. Because the answers of a language model are formulated in an extremely convincing and fluent manner, subtle biases are not immediately noticeable. You can use a simple testing methodology:
1. The perspective swap (Counterfactual testing)
Ask a question where you swap demographic variables, names, or cultural contexts, but keep the core question identical. For example, ask the model to evaluate a business plan for a person with a typical Dutch background and compare this with a scenario in which you use other cultural markers. If the tone, the advised risks, or the criticism differ significantly without a business reason, bias is present.
2. The null-hypothesis check
Check whether the model separates facts from opinions. Explicitly ask for multiple schools of thought or scientific consensus on social or economic issues. If the model immediately chooses a single narrative as absolute truth, it ignores the underlying nuance of the training data.
Realistic mitigation strategies
You cannot simply erase the base training of a commercial or open-source model. However, there are effective ways to significantly limit the impact of bias in your own applications:
- Use Retrieval-Augmented Generation (RAG): By applying a RAG architecture, you force the model to answer based on pre-selected, verified source documents instead of relying on the general, potentially biased internet knowledge of the model.
- Set explicit steering rules (System Prompts): Instruct the model in the system prompt to answer objectively, neutrally, and from multiple perspectives. For example: "For sensitive topics, always show different perspectives and avoid cultural stereotypes."
- Choose the right model: Not every model is trained in the same way. Some open-source models are specifically optimized for European regulations and multilingual diversity, making them less susceptible to Anglocentric dominance than older generations of models.
Conclusion
Bias in language models is not a conspiracy, but a statistical side effect of the data they were raised on. Those who are aware of the dominance of the English language area and the inherent limitations of raw internet data can take targeted measures. By testing critically, setting sharp system prompts, and relying on controlled knowledge sources via RAG, you transform a vulnerable, biased response into a reliable and usable output.