3: Shows thorough understanding of how the effects of human bias on training can be minimised. 2: Outlines some effects of human bias on ML OR features of training. 1: Some relevant information.

Training data is initially selected by humans, who may unintentionally introduce their own biases. To minimise this, ensure that training data is representative of the problem the ML model will address. Reviewing data by individuals from diverse backgrounds could identify and reduce potential biases, ensuring fairer and accurate model performance.