  Graph filters are an emerging paradigm that systematizes information
propagation in graphs as transformation of prior node values, called graph
signals, to posterior scores. In this work, we study the problem of mitigating
disparate impact, i.e. posterior score differences between a protected set of
sensitive nodes and the rest, while minimally editing scores to preserve
recommendation quality. To this end, we develop a scheme that respects
propagation mechanisms by editing graph signal priors according to their
posteriors and node sensitivity, where a small number of editing parameters can
be tuned to constrain or eliminate disparate impact. We also theoretically
explain that coarse prior editing can locally optimize posteriors objectives
thanks to graph filter robustness. We experiment on a diverse collection of 12
graphs with varying number of nodes, where our approach performs equally well
or better than previous ones in minimizing disparate impact and preserving
posterior AUC under fairness constraints.

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