  We study fairness in Machine Learning (FairML) through the lens of
attribute-based explanations generated for machine learning models. Our
hypothesis is: Biased Models have Biased Explanations. To establish that, we
first translate existing statistical notions of group fairness and define these
notions in terms of explanations given by the model. Then, we propose a novel
way of detecting (un)fairness for any black box model. We further look at
post-processing techniques for fairness and reason how explanations can be used
to make a bias mitigation technique more individually fair. We also introduce a
novel post-processing mitigation technique which increases individual fairness
in recourse while maintaining group level fairness.
