AWS Certified AI Practitioner AIF-C01 Practice Question

In machine learning, bias is a systematic error introduced by the data or the learning algorithm that consistently skews predictions toward or away from certain outcomes or groups. This is different from random noise, which is unpredictable error, and from overfitting, which is a consequence of excessive model complexity. Bias is also not defined as the numerical gap between training and validation loss; such a gap may be caused by bias, variance, or other issues, but it is not itself the definition of bias.