Fair Scheduling and Resource Allocation for Public Services

Public services (like health inspections, mail delivery, and street sweeping) are essential operations that a government provides to address the needs and well-being of communities. Efficient and fair outcomes of such operations are needed to ensure the resources are well-utilized and their benefit is distributed fairly. In this thesis, we study the tradeoffs between efficiency and fairness for prediction, allocation, and ranking tasks. First, we dive deep into the real-world application of a predictive model used by the Chicago Department of Public Health to schedule restaurant inspections and uncover the cause of unfairness, constituting geographic inequities in inspection health outcomes linked to the sanitarians conducting the inspection. Next, we delve into the broader context of efficiency and fairness in resource allocation problems, recognizing their inherent conflict. Drawing motivation from the effect of resources on group utilities, we frame our study within the context of homogeneous functions. We explore the intricate tradeoff between efficiency, fairness, and resources. Independent of allocation mechanisms, we find that the choice of evaluation measures leads to widely diverse conclusions about the effect of resources on efficiency and fairness. Finally, we show the inherent gaps in scoring-based ranking methods for achieving the optimal tradeoff between efficiency and fairness. We propose heuristic methods that perform better at varying tradeoff levels and propose a new direction for future research.

Author dc:creator

• Shubham Singh (704067)

• Computer Science

Statement dc:rights

• In Copyright

DOI dc:identifier

https://doi.org/10.25417/uic.31451131.v1

OAI identifier oai:identifier

oai:figshare.com:article/31451131