Generalized Disparate Impact for Configurable Fairness Solutions in ML
Luca Giuliani Eleonora Misino Michele Lombardi
We make two contributions in the field of AI fairness over continuous
protected attributes. First, we show that the Hirschfeld-Gebelein-Renyi (HGR)
indicator (the only one currently available for such a case) is valuable but
subject to a few crucial limitations regarding semantics, interpretability, and
robustness. Second, we introduce a family of indicators that are: 1)
complementary to HGR in terms of semantics; 2) fully interpretable and
transparent; 3) robust over finite samples; 4) configurable to suit specific
applications. Our approach also allows us to define fine-grained constraints to
permit certain types of dependence and forbid others selectively. By expanding
the available options for continuous protected attributes, our approach
represents a significant contribution to the area of fair artificial
intelligence.