Large language models (LLMs) operate by predicting the most likely next token based on patterns in their training data. This probabilistic nature contributes to algorithmic coloniality because these models are predominantly trained on massive datasets from the Global North, particularly Western legal texts and English-language sources. Consequently, the model learns to prioritize Western legal logic, terminology, and cultural norms as the universal standard for what constitutes correct legal reasoning.
When these models are used to automate legal research or decision-making, they may inadvertently erase or marginalize indigenous legal systems and non-Western traditions. Because the model predicts based on frequency rather than truth or cultural nuance, it reinforces existing power imbalances by treating dominant Western legal doctrines as the default. This creates a feedback loop where digital tools perpetuate a single way of understanding justice, making it harder for diverse legal perspectives to exist or be recognized in an automated landscape.
To mitigate this, researchers suggest diversifying training data and developing models that account for pluriversal legal frameworks. Understanding this risk is essential for anyone using AI in legal practice to ensure justice remains culturally inclusive and equitable.
