Some of the foundational papers in this area highlighted the limitations of trying to mitigate bias using methods that are "unaware" of protected attributes such as race, gender, or age (e.g., Dwork et al., 2012).
subsequent work has primarily focused on introducing fairness constraints, defined in terms of protected attributes, that reduce incentives to rely on undesirable correlations (e.g., Hardt et al., 2016;Zhang et al., 2018).