What challenge might arise from bias in training data?

Bias in training data can lead to the generation of irrelevant or stereotypical outputs because the model learns from the data it is trained on. If the training dataset contains biased samples or reflects existing stereotypes, the generative AI will replicate those biases in its outputs. This means that when the model generates content, it may produce results that reinforce stereotypes or lack diversity and nuance. As a consequence, such outputs might not accurately represent the intended audience or context, potentially misguiding users or perpetuating harmful narratives. This is a critical issue in AI, as it directly affects the quality, inclusiveness, and trustworthiness of the content generated by AI systems.