What biases exist in LLMs?

Large language models (LLMs) exhibit biases stemming from their training data, design choices, and interaction patterns. These biases often reflect societal, cultural, and historical imbalances present in the data they’re trained on. Because LLMs learn patterns from vast amounts of text (e.g., books, websites, social media), they inadvertently absorb and amplify stereotypes, misrepresentations, and skewed perspectives embedded in that data. For example, models might associate certain professions with specific genders (e.g., “nurse” linked to female pronouns, “engineer” to male) or reinforce racial stereotypes in generated text. These issues persist because training data rarely represents diverse viewpoints equally.

One major category of bias is social and cultural bias. LLMs often reflect dominant cultural norms, marginalizing underrepresented groups. For instance, a model might generate answers that assume Western-centric perspectives on topics like holidays, governance, or social customs, even when asked about other regions. Similarly, models trained predominantly on English-language data may perform poorly in representing non-Western languages or dialects, leading to inaccurate translations or culturally insensitive outputs. A concrete example is when an LLM translates “doctor” into a language with gendered terms and defaults to male pronouns, despite female doctors being common. These biases can alienate users from diverse backgrounds and limit the model’s global applicability.

Another key issue is representation and confirmation bias. LLMs tend to overrepresent majority viewpoints while underserving minority groups. For example, medical information generated by a model might focus on symptoms common in certain demographics (e.g., lighter skin tones for rashes) while ignoring others, leading to gaps in accuracy. Additionally, models can reinforce confirmation bias by prioritizing popular but incorrect information. If a model is trained on forums where climate change denial is frequent, it might generate answers that downplay scientific consensus. These biases are exacerbated by feedback loops: if users upvote biased outputs during fine-tuning, the model learns to reproduce them. Developers must proactively audit training data, test outputs across diverse scenarios, and implement safeguards to mitigate these risks.