What does the Shuffle mode not do?

The Shuffle mode is designed to enhance the training process by presenting data in a random order. This randomness helps in various ways, such as preventing the model from encountering data in a fixed and potentially biased sequence, which can influence its learning negatively. By mixing up the order of the training examples, Shuffle mode effectively supports a more diverse representation of input data, which can improve the model's robustness and generalization capabilities.

Choosing an approach that increases bias in training data runs counter to the objective of Shuffle mode. Increasing bias would undermine the process of training the model on a varied set of examples, ultimately leading to a less effective AI model. Good training practices aim for reduced bias by ensuring that the model learns from a representative sample of data, which helps prevent overfitting and helps the model perform better on unseen data.

While the other options relate to aspects of how Shuffle mode can contribute positively to training—by presenting random examples, focusing on low-confidence predictions, and promoting a balanced selection of data—the option about increasing bias stands out because it is fundamentally against the purpose of Shuffle mode and effective model training practices.