Predicting Criminal Recidivism Using Specialized Feature Engineering and XGBoost

Rajendran, Suraj; Sundararajan, P.

doi:10.21428/cb6ab371.d95f8c48

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…As part of the challenge, many ML-based recidivism prediction models were created [5]- [7] with the same data that this work utilizes. However, the models developed in this paper target an overall recidivism prediction, while the challenge models target a recidivism prediction for a particular year of the data [6].…”

Section: Prior Workmentioning

confidence: 99%

Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models

Ingram¹,

Gursoy²,

Kakadiaris³

2022

Preprint

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Criminal recidivism models are tools that have gained widespread adoption by parole boards across the United States to assist with parole decisions. These models take in large amounts of data about an individual and then predict whether an individual would commit a crime if released on parole. Although such models are not the only or primary factor in making the final parole decision, questions have been raised about their accuracy, fairness, and interpretability. In this paper, various machine learning-based criminal recidivism models are created based on a real-world parole decision dataset from the state of Georgia in the United States. The recidivism models are comparatively evaluated for their accuracy, fairness, and interpretability. It is found that there are noted differences and trade-offs between accuracy, fairness, and being inherently interpretable. Therefore, choosing the best model depends on the desired balance between accuracy, fairness, and interpretability, as no model is perfect or consistently the best across different criteria.

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Section: Prior Workmentioning

confidence: 99%