Exploring the Legal Accuracy of Mathematical Models in Crime Sentencing Prediction in the Face of Criminal Procedure Law Practice Needs

[1] Park, A. L. (2019). Injustice ex machina: Predictive algorithms in criminal sentencing. UCLA Law Review, 19. Search in Google Scholar

[2] Bagaric, M., & Wolf, G. (2017). Sentencing by computer: Enhancing sentencing transparency and predictability and (possibly) bridging the Gap between sentencing knowledge and practice. Geo. Mason L. Rev., 25, 653. Search in Google Scholar

[3] Liu, Y. H., & Chen, Y. L. (2018). A two-phase sentiment analysis approach for judgement prediction. Journal of Information Science, 44(5), 594-607. Search in Google Scholar

[4] Collins, E. (2018). Punishing risk. Geo. LJ, 107, 57. Search in Google Scholar

[5] Wang, C., Han, B., Patel, B., & Rudin, C. (2023). In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction. Journal of Quantitative Criminology, 39(2), 519-581. Search in Google Scholar

[6] Grgic-Hlaca, N., Redmiles, E. M., Gummadi, K. P., & Weller, A. (2018, April). Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction. In Proceedings of the 2018 world wide web conference (pp. 903-912). Search in Google Scholar

[7] Wu, T. H., Kao, B., Cheung, A. S., Cheung, M. M., Wang, C., Chen, Y., ... & Cheng, R. (2020). Integrating domain knowledge in ai-assisted criminal sentencing of drug trafficking cases. In Legal Knowledge and Information Systems (pp. 174-183). IOS Press. Search in Google Scholar

[8] Oleson, J. C., Lowenkamp, C. T., Wooldredge, J., VanNostrand, M., & Cadigan, T. P. (2017). The sentencing consequences of federal pretrial supervision. Crime & Delinquency, 63(3), 313-333. Search in Google Scholar

[9] Carlson, A. M. (2017). The need for transparency in the age of predictive sentencing algorithms. Iowa L. Rev., 103, 303. Search in Google Scholar

[10] Chiao, V. (2018). Predicting proportionality: The case for algorithmic sentencing. Criminal Justice Ethics, 37(3), 238-261. Search in Google Scholar

[11] Brayne, S., & Christin, A. (2021). Technologies of crime prediction: The reception of algorithms in policing and criminal courts. Social problems, 68(3), 608-624. Search in Google Scholar

[12] Lyu, Y., Wang, Z., Ren, Z., Ren, P., Chen, Z., Liu, X., ... & Song, H. (2022). Improving legal judgment prediction through reinforced criminal element extraction. Information Processing & Management, 59(1), 102780. Search in Google Scholar

[13] Tonry, M. (2019). Predictions of dangerousness in sentencing: Déjà vu all over again. Crime and Justice, 48(1), 439-482. Search in Google Scholar

[14] McKay, C. (2020). Predicting risk in criminal procedure: actuarial tools, algorithms, AI and judicial decision-making. Current Issues in Criminal Justice, 32(1), 22-39. Search in Google Scholar

[15] Li Yazi,Wei Xiaoman,Yang Qinglin,Xiong An,Li Xingfeng,Zou Quan... & Zhang Zilong.(2024). msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths.BMC Biology(1),126-126. Search in Google Scholar

[16] Allaert Jens,Erdogan Maide,Sanchez Lopez Alvaro,Baeken Chris,De Raedt Rudi & Vanderhasselt Marie Anne.(2021).Prefrontal tDCS Attenuates Self-Referential Attentional Deployment: A Mechanism Underlying Adaptive Emotional Reactivity to Social-Evaluative Threat .Frontiers in Human Neuroscience 700557-700557. Search in Google Scholar

[17] Mehdaoui Ibrahim,Boudibi Samir,Latif Sarmad Dashti,Sakaa Bachir,Chaffai Hicham & Hani Azzedine. (2024). Prediction of nitrate concentrations using multiple linear regression and radial basis function neural network in the Cheliff River basin, Algeria.Journal of Applied Water Engineering and Research(1),77-89. Search in Google Scholar

[18] Lubomír Kubáček & Gejza Wimmer.(2016).Reparameterization of weakly nonlinear regression models with constraints.Mathematica Slovaca(3),731-744. Search in Google Scholar