Bias in machine learning (and statistics) is the difference between the average predicted value of a model and the positive value it is trying to predict.
A model with a high bias is in a state of under-learning, where the relationship between the inputs to the model and the outputs has not been accurately learned, and even the training data cannot be accurately predicted.
This condition is called under-fitting.
Relationship between bias and variance
As mentioned above, if the bias is too high, we will end up in an under-learning state.
So, when we try to lower the bias, the variance goes up.
This relationship is called a trade-off between bias and variance.
For more information on the trade-off between bias and variance, please refer to this blog.
Bias and Variance Tradeoff