  Most existing notions of algorithmic fairness are one-shot: they ensure some
form of allocative equality at the time of decision making, but do not account
for the adverse impact of the algorithmic decisions today on the long-term
welfare and prosperity of certain segments of the population. We take a broader
perspective on algorithmic fairness. We propose an effort-based measure of
fairness and present a data-driven framework for characterizing the long-term
impact of algorithmic policies on reshaping the underlying population.
Motivated by the psychological literature on \emph{social learning} and the
economic literature on equality of opportunity, we propose a micro-scale model
of how individuals may respond to decision-making algorithms. We employ
existing measures of segregation from sociology and economics to quantify the
resulting macro-scale population-level change. Importantly, we observe that
different models may shift the group-conditional distribution of qualifications
in different directions. Our findings raise a number of important questions
regarding the formalization of fairness for decision-making models.
