  Data valuation is a ML field that studies the value of training instances
towards a given predictive task. Although data bias is one of the main sources
of downstream model unfairness, previous work in data valuation does not
consider how training instances may influence both performance and fairness of
ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data
valuation framework that can be used to incorporate fairness concerns into a
series of ML-related tasks (e.g., data pre-processing, exploratory data
analysis, active learning). We propose an entropy-based data valuation metric
suited to address our two-pronged goal of maximizing both performance and
fairness, which is more computationally efficient than existing metrics. We
then show how FADO can be applied as the basis for unfairness mitigation
pre-processing techniques. Our methods achieve promising results -- up to a 40
p.p. improvement in fairness at a less than 1 p.p. loss in performance compared
to a baseline -- and promote fairness in a data-centric way, where a deeper
understanding of data quality takes center stage.
