Active Fairness Instead of Unawareness
  The possible risk that AI systems could promote discrimination by reproducing
and enforcing unwanted bias in data has been broadly discussed in research and
society. Many current legal standards demand to remove sensitive attributes
from data in order to achieve "fairness through unawareness". We argue that
this approach is obsolete in the era of big data where large datasets with
highly correlated attributes are common. In the contrary, we propose the active
use of sensitive attributes with the purpose of observing and controlling any
kind of discrimination, and thus leading to fair results.
