# Fair Prediction with Endogenous Behavior

arXiv: https://arxiv.org/abs/2002.07147 · HTML: https://mlsanity.com/paper/2002.07147/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Modeling Techniques for Machine Learning Fairness: A Survey](https://mlsanity.com/paper/2111.03015/) (2021)
- [Fairness in Forecasting of Observations of Linear Dynamical Systems](https://mlsanity.com/paper/2209.05274/) (2022)
- [Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification](https://mlsanity.com/paper/2110.00813/) (2021)
- [Critical Appraisal of Fairness Metrics in Clinical Predictive AI](https://mlsanity.com/paper/2506.17035/) (2025)
- [The Disparate Effects of Partial Information in Bayesian Strategic Learning](https://mlsanity.com/paper/2506.00627/) (2025)
- [Using Property Elicitation to Understand the Impacts of Fairness Regularizers](https://mlsanity.com/paper/2309.11343/) (2023)
- [Interventions Against Machine-Assisted Statistical Discrimination](https://mlsanity.com/paper/2310.04585/) (2023)
- [Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteria](https://mlsanity.com/paper/2403.08564/) (2024)
- [Revisiting Fairness Impossibility with Endogenous Behavior](https://mlsanity.com/paper/2604.06378/) (2026)

## Abstract (arXiv)

There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fairly." However, there are several proposed notions of fairness, typically mutually incompatible. Using criminal justice as an example, we study a model in which society chooses an incarceration rule. Agents of different demographic groups differ in their outside options (e.g. opportunity for legal employment) and decide whether to commit crimes. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate; other popular notions of fairness are not.