What does "acquisition bias" in generative models refer to?

Acquisition bias in generative models refers to the scenario where the outputs generated by the model are affected by the characteristics of the training data it has been exposed to. If the training dataset is non-representative, meaning it does not accurately reflect the diversity or range of real-world scenarios, the model may produce skewed or biased results. This can happen if certain groups, features, or perspectives are underrepresented or overrepresented in the training data, leading to a generative model that does not generalize well to new, unseen data. The resulting outputs may reflect these biases, resulting in outcomes that can reinforce stereotypes or omit important variations.

This understanding underlines the importance of curating diverse and representative datasets when training generative models to minimize bias and enhance the overall quality and applicability of the generated results.