I would just like to make clear that what you are trying to study is what you posted in the question.
In machine learning as a subfield of AI there is algorithmic bias and data bias. To give you an example of algorithmic bias, take for example the following quote:
"The linear decision boundary from least squares is very smooth, and apparently stable to fit. It does appear to rely heavily on the assumption that a linear decision boundary is appropriate. In language we will develop shortly, it has low variance and potentially high bias." [1, p.16].
This is different from data bias which is domain dependent and has made headlines with Amazon's HR[2]. The article by Qamar Ul Islam is a good point to start in this area.
In terms of whether it is a mediating variable or construct , it depends on how you define the phenomena. That is, Whether it is defined as just bias of the data or underlying sampling of prejudice (which is more abstract).
[1]The Elements of Statistical Learning Data Mining, Inference, and Prediction by