# Hindsight Analysis of the Chicago Food Inspection Forecasting Model

arXiv: https://arxiv.org/abs/1910.04906 · HTML: https://mlsanity.com/paper/1910.04906/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Fair Decision-Making for Food Inspections](https://mlsanity.com/paper/2108.05523/) (2021)
- [Optimizing Urban Service Allocation with Time-Constrained Restless Bandits](https://mlsanity.com/paper/2502.00045/) (2025)
- [Continuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals](https://mlsanity.com/paper/2610.15191/) (2026)

## Abstract (arXiv)

The Chicago Department of Public Health (CDPH) conducts routine food inspections of over 15,000 food establishments to ensure the health and safety of their patrons. In 2015, CDPH deployed a machine learning model to schedule inspections of establishments based on their likelihood to commit critical food code violations. The City of Chicago released the training data and source code for the model, allowing anyone to examine the model. We provide the first independent analysis of the model, the data, the predictor variables, the performance metrics, and the underlying assumptions. We present a summary of our findings, share lessons learned, and make recommendations to address some of the issues our analysis unearthed.