# Fair Decision-Making for Food Inspections

arXiv: https://arxiv.org/abs/2108.05523 · HTML: https://mlsanity.com/paper/2108.05523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [Policy Learning for Fairness in Ranking](https://mlsanity.com/paper/1902.04056/) (2019)
- [Algorithms for Fairness in Sequential Decision Making](https://mlsanity.com/paper/1901.08568/) (2019)
- [Fairness Under Composition](https://mlsanity.com/paper/1806.06122/) (2018)
- [A statistical framework for fair predictive algorithms](https://mlsanity.com/paper/1610.08077/) (2016)
- [Fairness in Ranking: A Survey](https://mlsanity.com/paper/2103.14000/) (2021)
- [Individual Fairness in Pipelines](https://mlsanity.com/paper/2004.05167/) (2020)
- [The Sanction of Authority: Promoting Public Trust in AI](https://mlsanity.com/paper/2102.04221/) (2021)
- [The Price of Local Fairness in Multistage Selection](https://mlsanity.com/paper/1906.06613/) (2019)
- [Fair for All: Best-effort Fairness Guarantees for Classification](https://mlsanity.com/paper/2012.10216/) (2020)
- [Machine-learned epidemiology: real-time detection of foodborne illness at scale](https://mlsanity.com/paper/1812.01813/) (2018)
- [Hindsight Analysis of the Chicago Food Inspection Forecasting Model](https://mlsanity.com/paper/1910.04906/) (2019)
- [Fairness Through Counterfactual Utilities](https://mlsanity.com/paper/2108.05315/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimizing Urban Service Allocation with Time-Constrained Restless Bandits](https://mlsanity.com/paper/2502.00045/) (2025)

## Abstract (arXiv)

Data and algorithms are essential and complementary parts of a large-scale decision-making process. However, their injudicious use can lead to unforeseen consequences, as has been observed by researchers and activists alike in the recent past. In this paper, we revisit the application of predictive models by the Chicago Department of Public Health to schedule restaurant inspections and prioritize the detection of critical food code violations. We perform the first analysis of the model's fairness to the population served by the restaurants in terms of average time to find a critical violation. We find that the model treats inspections unequally based on the sanitarian who conducted the inspection and that, in turn, there are geographic disparities in the benefits of the model. We examine four alternate methods of model training and two alternative ways of scheduling using the model and find that the latter generate more desirable results. The challenges from this application point to important directions for future work around fairness with collective entities rather than individuals, the use of critical violations as a proxy, and the disconnect between fair classification and fairness in the dynamic scheduling system.