Causal Modeling for Fairness in Dynamical Systems [article]

Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel
2020 arXiv   pre-print
In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups. We discuss causal directed acyclic graphs (DAGs) as a unifying framework for the recent literature on fairness in such dynamical systems. We show that this formulation affords several new directions of
more » ... y to the modeler, where causal assumptions can be expressed and manipulated. We emphasize the importance of computing interventional quantities in the dynamical fairness setting, and show how causal assumptions enable simulation (when environment dynamics are known) and off-policy estimation (when dynamics are unknown) of intervention on short- and long-term outcomes, at both the group and individual levels.
arXiv:1909.09141v2 fatcat:kt33xsq3tffvvoigxgvj4ngcme