What does the term "model bias" refer to in generative AI?

The term "model bias" specifically refers to a situation where a generative AI model produces outputs that reflect certain prejudices, stereotypes, or imbalances that are present in the training data. This occurs because the model learns patterns and associations from the data it is trained on. If the dataset includes biased information—whether intentionally or unintentionally—the model may perpetuate or amplify these biases when generating outputs.

For example, if a training dataset has more representations of certain demographics over others, the model might develop a skewed understanding that favors the overrepresented groups, leading to biased AI outcomes. Thus, the acknowledgment and mitigation of model bias are crucial in ensuring fair and ethical AI practices, as it directly impacts the quality and acceptance of the generated outputs in real-world applications.