> Ahead of the International Women's Day, a UNESCO study revealed worrying tendencies in Large Language models (LLM) to produce gender bias, as well as homophobia and racial stereotyping. Women were described as working in domestic roles far more often than men ¬– four times as often by one model – and were frequently associated with words like “home”, “family” and “children”, while male names were linked to “business”, “executive”, “salary”, and “career”.

https://www.unesco.org/en/articles/generative-ai-unesco-stud...

> Our analysis proves that bias in LLMs is not an unintended flaw but a systematic result of their rational processing, which tends to preserve and amplify existing societal biases encoded in training data. Drawing on existentialist theory, we argue that LLM-generated bias reflects entrenched societal structures and highlights the limitations of purely technical debiasing methods.

> We find that the portrayals generated by GPT-3.5 and GPT-4 contain higher rates of racial stereotypes than human-written por- trayals using the same prompts. The words distinguishing personas of marked (non-white, non-male) groups reflect patterns of othering and exoticizing these demographics. An inter- sectional lens further reveals tropes that domi- nate portrayals of marginalized groups, such as tropicalism and the hypersexualization of mi- noritized women. These representational harms have concerning implications for downstream applications like story generation.

https://aclanthology.org/2023.acl-long.84.pdf