  The unparalleled ability of machine learning algorithms to learn patterns
from data also enables them to incorporate biases embedded within. A biased
model can then make decisions that disproportionately harm certain groups in
society. Much work has been devoted to measuring unfairness in static ML
environments, but not in dynamic, performative prediction ones, in which most
real-world use cases operate. In the latter, the predictive model itself plays
a pivotal role in shaping the distribution of the data. However, little
attention has been heeded to relating unfairness to these interactions. Thus,
to further the understanding of unfairness in these settings, we propose a
taxonomy to characterize bias in the data, and study cases where it is shaped
by model behaviour. Using a real-world account opening fraud detection case
study as an example, we study the dangers to both performance and fairness of
two typical biases in performative prediction: distribution shifts, and the
problem of selective labels.
