@conference {1500319,
	title = {Designing Fair, Efficient, and Interpretable Policies for Prioritizing Homeless Youth for Housing Resources},
	booktitle = {International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
	year = {2018},
	abstract = { We consider the problem of designing fair, efficient, and interpretable policies for prioritizing heterogeneous homeless youth on a
waiting list for scarce housing resources of different types. We focus on
point-based policies that use features of the housing resources (e.g., permanent supportive housing, rapid rehousing) and the youth (e.g., age,
history of substance use) to maximize the probability that the youth will
have a safe and stable exit from the housing program. The policies can
be used to prioritize waitlisted youth each time a housing resource is
procured. Our framework provides the policy-maker the flexibility to select both their desired structure for the policy and their desired fairness
requirements. Our approach can thus explicitly trade-off interpretability
and efficiency while ensuring that fairness constraints are met. We propose a flexible data-driven mixed-integer optimization formulation for
designing the policy, along with an approximate formulation which can
be solved efficiently for broad classes of interpretable policies using Bender{\textquoteright}s decomposition. We evaluate our framework using real-world data
from the United States homeless youth housing system. We show that
our framework results in policies that are more fair than the current policy in place and than classical interpretable machine learning approaches
while achieving a similar (or higher) level of overall efficiency.},
	author = {Azizi, Mohammad Javad and Vayanos, Phebe and Bryan Wilder and Eric Rice and Tambe, Milind}
}
