That is yet a different meaning of the word “bias”: that is closer to the classic definition of that word that you will find if you look it up in the dictionary. An example would be if you are trying to build a recommender system to recommend movies that someone might like and you only collect data from people under 30 years old. Then the system will do a bad job of making recommendations for people over the age of 60, so that dataset is biased towards the preferences of younger people. You have to make sure that the data you use to train your system represents the full range of the things you need the system to predict.
Then in math they use “bias” to mean the b term in a linear expression like:
y = mx + b
And then in ML, there is yet a different definition which is what Mubsi and others have been describing on this thread in the “bias/variance” tradeoff discussion.