TY - GEN
T1 - Accuracy-Fairness Tradeoff in Parole Decision Predictions
T2 - 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
AU - Gardner, John W.
AU - Gursoy, Furkan
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
ACKNOWLEDGMENT 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 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 - Algorithms play an essential and expanding role in public policy decisions, including those in criminal justice. This short paper reports on the first author's summer research project characterizing the tradeoff between accuracy and fairness in parole decision predictions. The dataset employed in this study contains over 30,000 parole decisions made by the New York State Division of Criminal Justice Services. Each decision contains information on the subject, such as sex, race/ethnicity, and parole decision, as well as predictive features describing the crime committed by the subject and the parole interview held. Logistic regression, decision tree, support vector machine, and random forest models are trained and utilized to analyze parole decision predictions based on the available features. Most models fail to pass standard fairness tests for most fairness metrics. Moreover, while there may be an overall tradeoff between fairness and accuracy, the obtained differences in accuracy are too small to make a well-supported claim. Future research may enhance the preliminary work introduced in this paper by using multiple real-world datasets to investigate the tradeoff between accuracy and fairness.
AB - Algorithms play an essential and expanding role in public policy decisions, including those in criminal justice. This short paper reports on the first author's summer research project characterizing the tradeoff between accuracy and fairness in parole decision predictions. The dataset employed in this study contains over 30,000 parole decisions made by the New York State Division of Criminal Justice Services. Each decision contains information on the subject, such as sex, race/ethnicity, and parole decision, as well as predictive features describing the crime committed by the subject and the parole interview held. Logistic regression, decision tree, support vector machine, and random forest models are trained and utilized to analyze parole decision predictions based on the available features. Most models fail to pass standard fairness tests for most fairness metrics. Moreover, while there may be an overall tradeoff between fairness and accuracy, the obtained differences in accuracy are too small to make a well-supported claim. Future research may enhance the preliminary work introduced in this paper by using multiple real-world datasets to investigate the tradeoff between accuracy and fairness.
KW - Accuracy
KW - Fairness
KW - Machine Learning
KW - Parole Decisions
KW - Tradeoff
UR - http://www.scopus.com/inward/record.url?scp=85150683698&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150683698&partnerID=8YFLogxK
U2 - 10.1109/BDCAT56447.2022.00047
DO - 10.1109/BDCAT56447.2022.00047
M3 - Conference contribution
AN - SCOPUS:85150683698
T3 - Proceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
SP - 284
EP - 287
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.
Y2 - 6 December 2022 through 9 December 2022
ER -