Endogenous Macrodynamics in Algorithmic Recourse
Patrick Altmeyer Giovan Angela Aleksander Buszydlik Karol Dobiczek Arie van Deursen Cynthia C. S. Liem
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse
(AR) has largely focused on single individuals in a static environment: given
some estimated model, the goal is to find valid counterfactuals for an
individual instance that fulfill various desiderata. The ability of such
counterfactuals to handle dynamics like data and model drift remains a largely
unexplored research challenge. There has also been surprisingly little work on
the related question of how the actual implementation of recourse by one
individual may affect other individuals. Through this work, we aim to close
that gap. We first show that many of the existing methodologies can be
collectively described by a generalized framework. We then argue that the
existing framework does not account for a hidden external cost of recourse,
that only reveals itself when studying the endogenous dynamics of recourse at
the group level. Through simulation experiments involving various state-of
the-art counterfactual generators and several benchmark datasets, we generate
large numbers of counterfactuals and study the resulting domain and model
shifts. We find that the induced shifts are substantial enough to likely impede
the applicability of Algorithmic Recourse in some situations. Fortunately, we
find various strategies to mitigate these concerns. Our simulation framework
for studying recourse dynamics is fast and opensourced.