Recent research indicates that large language models (LLMs) exhibit social identity biases, paralleling biases observed in humans. This opens up new discussions regarding the ethical implications of AI and the need for more robust training methodologies. Fortunately, the study notes that these biases can potentially be mitigated by carefully curating the training data, focusing on representation and inclusivity. Companies and developers need to be proactive in addressing these issues to ensure fairer AI applications that do not perpetuate harmful stereotypes or discrimination.