26: Auditing Machine Learning Systems for Equitable Community Resource Allocation

Thomas M. Menino Convention & Exhibition Center 

Local governments are increasingly using machine learning to manage community resources, from prioritizing road repairs to mapping out public transit routes. However, these algorithms risk reinforcing or even worsening existing inequalities between neighborhoods of different backgrounds. This study introduces a practical auditing method designed to catch and measure potential bias in these public-sector systems by combining standard statistical fairness metrics with spatial analysis across census tracts.
To test this, I analyzed city datasets-specifically infrastructure service requests and transit operations-using gradient boosting and random forest models. I evaluated predictive accuracy and service outcomes across neighborhoods grouped by income and demographics. My findings show significant disparities; lower-resourced areas often face different treatment from models, even when their needs are similar to wealthier areas.
Finally, I explored how to bridge these gaps using post-hoc calibration strategies, demonstrating meaningful reductions in fairness disparities. This research offers a practical roadmap and provides evidence needed to build more equitable community services.

algorithmic fairness

community equity

machine learning auditing

municipal data

bias detection

spatial statistics 

Section on Statistical Computing