From the fair distribution of predictions to the fair distribution of social goods: Evaluating the impact of fair machine learning on long-term unemployment

Sebastian Zezulka, Konstantin Genin

Abstract:

Deploying an algorithmically informed policy is a significant in-
tervention in society. Prominent methods for algorithmic fairness
focus on the distribution of predictions at the time of training, rather
than the distribution of social goods that arises after deploying the
algorithm in a specific social context. However, requiring a ‘fair’
distribution of predictions may undermine efforts at establishing a
fair distribution of social goods. First, we argue that addressing this
problem requires a notion of prospective fairness that anticipates
the change in the distribution of social goods after deployment.
Second, we provide formal conditions under which this change
is identified from pre-deployment data. That requires accounting
for different kinds of performative effects. Here, we focus on the
way predictions change policy decisions and, consequently, the
causally downstream distribution of social goods. Throughout, we
are guided by an application from public administration: the use
of algorithms to predict who among the recently unemployed will
remain unemployed in the long term and to target them with labor
market programs. Third, using administrative data from the Swiss
public employment service, we simulate how such algorithmically
informed policies would affect gender inequalities in long-term
unemployment. When risk predictions are required to be ‘fair’ ac-
cording to statistical parity and equality of opportunity, targeting
decisions are less effective, undermining efforts to both lower over-
all levels of long-term unemployment and to close the gender gap
in long-term unemployment.