Which method is NOT commonly used to mitigate bias in Generative AI?

The method of ignoring dataset composition is not commonly used to mitigate bias in Generative AI because neglecting how data is collected and represented can lead to perpetuating and amplifying existing biases. In the context of machine learning and AI, the quality and diversity of the training data are critical factors that influence model performance and fairness. By ignoring how datasets are composed, AI systems may not fairly represent all groups, leading to biased outputs.

On the other hand, utilizing diverse datasets helps ensure that the training data encompasses a wide range of perspectives, thereby reducing bias. Implementing fairness algorithms directly targets bias by applying techniques that adjust the model’s decisions to promote fairness. Conducting regular audits of outputs is also a proactive approach to identifying and addressing any potential biases in AI systems by monitoring how the models perform across different demographics and use cases. Each of these methods contributes positively towards creating more equitable and fair AI systems, while ignoring the dataset composition may lead to harmful outcomes.