TY - GEN
T1 - Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models
AU - Ingram, Eric
AU - Gursoy, Furkan
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
The authors thank Dr. Ryan Kennedy for his valuable contributions to this study. The first author s work was supported by the University of Houston s Computer Science REU program that is primarily sponsored by the National Science Foundation under Award CCF-195029 and the University of Houston s College of Natural Sciences and Mathematics. Drs. Gursoy and Kakadiaris work was supported by the National Science Foundation under Award CCF-2131504. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Funding Information:
The authors thank Dr. Ryan Kennedyfor his valuablecontri-butions to this study. The first author’s work was supported by the University of Houston’s Computer Science REU program that is primarily sponsored by the National Science Foundation under Award CCF-195029 and the University of Houston’s College of Natural Sciences and Mathematics. Drs. Gursoy and Kakadiaris’ work was supported by the National Science Foundation under Award CCF-2131504. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Accuracy
KW - Criminal Recidivism
KW - Fairness
KW - Interpretability
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85150680512&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150680512&partnerID=8YFLogxK
U2 - 10.1109/BDCAT56447.2022.00040
DO - 10.1109/BDCAT56447.2022.00040
M3 - Conference contribution
AN - SCOPUS:85150680512
T3 - Proceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
SP - 233
EP - 241
BT - Proceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
Y2 - 6 December 2022 through 9 December 2022
ER -