The bottom line: Large Language Models reflect the weightings of their training data – those overrepresented in it, which perspectives are treated as standard, and which viewpoints are absent shape every output of the model.
Companies often treat AI models like electricity from a socket – without asking what’s inside them. Yet the training data, its origin, and the worldviews encoded within it have significant consequences for business and legal questions.
Companies are currently focused on use cases, efficiency gains, and pilot projects, but rarely ask questions about the composition of the underlying models. A Large Language Model is not a search engine or document management system, but the result of a training process: a system is fed enormous amounts of text and learns statistical patterns in the process – which words follow each other, which concepts are related, how language works in specific contexts. What is stored are not the texts themselves, but billions of numerical values (weights) that encode what the model knows.
The quality, selection, and origin of training data determine what capabilities a model has, what gaps it has, and which perspectives it treats as self-evident. The first problems emerge here: no established provider fully discloses its training data. At best, the origin can no longer be traced back; at worst, content was used without permission. Corresponding proceedings against virtually all major providers are pending. For companies that have built on these models, it is still unclear what consequences could arise – particularly if courts decide that certain training data was used illegally.
Training data transports not only knowledge but also attitudes and evaluations. In the texts from which a model learns, there is what is considered normal, what is classified as problematic, whose perspective is set as standard, and whose is set as exception. A language model trained predominantly with English-language sources from Western contexts has internalized these weightings – in the first examples it provides, in the associations it makes, in what it formulates neutrally or marks as problematic. This is not intentional, but rather the direct consequence of how frequently certain groups appear in the dataset and how they are written about. Discrimination does not arise here from bad intent, but from representation patterns.
This becomes critical when AI makes direct judgments about people – in candidate selection, credit decisions, or customer scoring. Companies that don’t know what worldview their models are based on will notice the problem at the latest when the first case goes to court. A board member or Chief Data Officer should therefore basically know what training data, licenses, and implicit perspectives lie behind the deployed model – before the first prompt is formulated.
An additional cost signal: LLM development today essentially takes place in two language spaces – English and Chinese. All other languages play a subordinate role, which is directly reflected in quality and pricing models. Those working in German or other languages pay for this structural disadvantage as well.
Source: www.it-daily.net · Published June 12, 2026
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