  Although several fairness definitions and bias mitigation techniques exist in
the literature, all existing solutions evaluate fairness of Machine Learning
(ML) systems after the training stage. In this paper, we take the first steps
towards evaluating a more holistic approach by testing for fairness both before
and after model training. We evaluate the effectiveness of the proposed
approach and position it within the ML development lifecycle, using an
empirical analysis of the relationship between model dependent and independent
fairness metrics. The study uses 2 fairness metrics, 4 ML algorithms, 5
real-world datasets and 1600 fairness evaluation cycles. We find a linear
relationship between data and model fairness metrics when the distribution and
the size of the training data changes. Our results indicate that testing for
fairness prior to training can be a ``cheap'' and effective means of catching a
biased data collection process early; detecting data drifts in production
systems and minimising execution of full training cycles thus reducing
development time and costs.
