Transparent and bias-resilient AI framework for recidivism prediction using deep learning and clustering techniques in criminal justice
Yazarlar (6)
Northumbria University, İngiltere
University of Aberdeen, İngiltere
Northumbria University, İngiltere
Dr. Öğr. Üyesi Mahmut SARI
Kırşehir Ahi Evran Üniversitesi, Türkiye
Newcastle University - UK, İngiltere
Newcastle University - UK, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | APPLIED SOFT COMPUTING (Q1) | ||
| Dergi ISSN | 1568-4946 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2025 |
| Cilt / Sayı / Sayfa | 176 / 0 / – | DOI | 10.1016/j.asoc.2025.113160 |
| Makale Linki | https://doi.org/10.1016/j.asoc.2025.113160 | ||
| Özet |
| This paper presents the Recidivism Clustering Network (RCN), an effective approach for predicting repeat offenses using deep learning (DL), clustering, and explainable AI (XAI). The RCN improves offender profiling for more accurate and interpretable recidivism predictions, aligning with key legal principles like fair sentencing, transparency, and non-discrimination. The RCN employs machine learning (ML) models optimized with a Keras tuner, using the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. With about 75% accuracy, the model shows strong recall, identifying 10,661 recidivists but producing 4,038 false positives--indicating a trade-off between sensitivity and specificity. Beyond predictions, RCN integrates clustering methods, including k-means, principal component analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE), to identify hidden patterns within ... |
| Anahtar Kelimeler |
| Deep learning | Recidivism prediction | Explainable AI | Criminal justice system |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 3 |
| Google Scholar | 5 |