{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T06:41:37Z","timestamp":1787899297495,"version":"build-2784847793"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Judicial decision-making in continental law systems requires carefully evaluating complex, interdependent evidence while ensuring consistency and fairness. This study investigates the application of Bayesian networks in structuring legal evidence within an AI-based decision support system. The primary research objective is to assess the enhancement of transparency, minimization of cognitive overload, and reduced bias in judicial processes through probabilistic reasoning. The proposed system dynamically updates outcome probabilities as new evidence is introduced, enabling real-time monitoring of case likelihoods. AI-generated recommendations are aligned with judicial precedents by integrating Conditional Probability Tables (CPTs) and historical case data in this adaptive approach. The interpretability and effectiveness of Bayesian inference in legal decision support are analyzed methodologically, emphasizing its capacity to refine probability distributions in response to evolving courtroom inputs. The findings address a key research gap by demonstrating how structured AI-driven heuristics can supplement judicial reasoning while maintaining decision accountability and transparency. The system is suggested to enhance consistency and fairness in legal judgments while preserving judicial autonomy. This study contributes to the growing intersection of AI and legal decision-making, with an emphasis placed on the role of machine learning in supporting judicial heuristics while maintaining procedural integrity.<\/jats:p>","DOI":"10.3390\/systems13020131","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T10:26:12Z","timestamp":1739787972000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["An AI-Based Decision Support System Utilizing Bayesian Networks for Judicial Decision-Making"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5566-1581","authenticated-orcid":false,"given":"Zlatan","family":"Mori\u0107","sequence":"first","affiliation":[{"name":"Department of Cybersecurity and System Engineering, Algebra University, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8638-6044","authenticated-orcid":false,"given":"Vedran","family":"Daki\u0107","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity and System Engineering, Algebra University, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4756-8571","authenticated-orcid":false,"given":"Sini\u0161a","family":"Uro\u0161ev","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity and System Engineering, Algebra University, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"ref_1","first-page":"328","article-title":"Prototype of Classifier for the Decision Support System of Legal Documents","volume":"2543","author":"Alekseev","year":"2019","journal-title":"Proc. Abrau Conf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Sun, C. (2021, January 27\u201328). Research on Judgment System of Legal Cases Based on Neural Network. Proceedings of the 2021 International Conference on Neural Networks, Information and Communication Engineering, Qingdao, China.","DOI":"10.1117\/12.2615184"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1287\/mnsc.1060.0514","article-title":"Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming","volume":"52","author":"Cui","year":"2006","journal-title":"Manag. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.4018\/IJEGR.2020100102","article-title":"Data Transcription for India\u2019s Supreme Court Documents Using Deep Learning Algorithms","volume":"16","author":"Vaissnave","year":"2020","journal-title":"Int. J. Electron. Gov. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/cogs.12004","article-title":"A General Structure for Legal Arguments About Evidence Using Bayesian Networks","volume":"37","author":"Fenton","year":"2012","journal-title":"Cogn. Sci."},{"key":"ref_6","first-page":"79","article-title":"Data Mining and Knowledge Discovery","volume":"1","author":"Heckerman","year":"1997","journal-title":"Springer Sci. Bus. Media LLC"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shelar, A., and Moharir, M. (2021, January 25\u201327). Predicting Outcomes of Court Judgments\u2014A Machine Learning Approach. Proceedings of the 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India.","DOI":"10.1109\/CONIT51480.2021.9498385"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vlek, C., Prakken, H., Renooij, S., and Verheij, B. (2015, January 8\u201312). Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes. Proceedings of the 15th International Conference on Artificial Intelligence and Law, San Diego, CA USA.","DOI":"10.1145\/2746090.2746097"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"117","DOI":"10.47345\/v11n1art6","article-title":"Interpretable AI Models for Judicial Decision-Making: Beyond Explicability Towards Legal Due Process","volume":"11","author":"Canalli","year":"2024","journal-title":"e-Publica"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Malek, M.A. (2021). Transparency in Predictive Algorithms: A Judicial Perspective. Advance, 1\u201313.","DOI":"10.31124\/advance.14699937.v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Scutari, M., and Denis, J.-B. (2014). Bayesian Networks, Chapman and Hall\/CRC.","DOI":"10.1201\/b17065"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s10506-016-9183-4","article-title":"A Method for Explaining Bayesian Networks for Legal Evidence with Scenarios","volume":"24","author":"Vlek","year":"2016","journal-title":"Artif. Intell. Law"},{"key":"ref_13","unstructured":"Timmer, S., Meyer, J., Prakken, H., Renooij, S., and Verheij, B. (2015, January 8\u201312). Explaining Legal Bayesian Networks Using Support Graphs. Proceedings of the 15th International Conference on Artificial Intelligence and Law, San Diego, CA USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/widm.6","article-title":"Data Mining and Crime Analysis","volume":"1","author":"Oatley","year":"2011","journal-title":"WIREs Data Min. Knowl."},{"key":"ref_15","unstructured":"Kwan, M., Chow, K., Law, F., and Lai, P. (2008, January 23\u201325). Reasoning About Evidence Using Bayesian Networks. Proceedings of the International Conference on Digital Forensics and Cyber Crime, London, UK."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Overill, R., Silomon, J.A.M., Kwan, M., Chow, K., Law, F., and Lai, P. (2010, January 11\u201313). Sensitivity Analysis of a Bayesian Network for Reasoning about Digital Forensic Evidence. Proceedings of the International Conference on Human-Centric Computing, Cebu, Philippines.","DOI":"10.1109\/HUMANCOM.2010.5563318"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cerotti, D., Codetta-Raiteri, D., Dondossola, G., Egidi, L., Franceschinis, G., Portinale, L., and Terruggia, R. (2020). Evidence-Based Analysis of Cyber Attacks to Security Monitored Distributed Energy Resources. Appl. Sci., 10.","DOI":"10.3390\/app10144725"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1109\/TII.2017.2768998","article-title":"A Fuzzy Probability Bayesian Network Approach for Dynamic Cybersecurity Risk Assessment in Industrial Control Systems","volume":"14","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"659","DOI":"10.3758\/s13423-011-0106-9","article-title":"Workload Capacity Spaces: A Unified Methodology for Response Time Measures of Efficiency as Workload is Varied","volume":"18","author":"Townsend","year":"2011","journal-title":"Psychon. Bull. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/13218719.2024.2343091","article-title":"Testing the Model of Judicial Stress Using a COVID-Era Survey of U.S. Federal Court Personnel","volume":"31","author":"Fine","year":"2024","journal-title":"Psychiatry Psychol. Law"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/12460125.2022.2070946","article-title":"Support for Cognition in Decision Support Systems: An Exploratory Historical Review","volume":"31","author":"Daly","year":"2022","journal-title":"J. Decis. Syst."},{"key":"ref_22","unstructured":"Khatri, M., Yusuf, M., Kumar, Y., Shah, R.R., and Kumaraguru, P. (2023). Exploring Graph Neural Networks for Indian Legal Judgment Prediction. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Delen, D., and Sharda, R. (2008). Artificial Neural Networks in Decision Support Systems. Handbook on Decision Support Systems 1, Springer.","DOI":"10.1007\/978-3-540-48713-5_26"},{"key":"ref_24","unstructured":"Umamaheswari, S., Aartisha, S., Kanimozhi, J., and Suhashini, R. (2023, January 16\u201317). Building Accurate Legal Case Outcome Prediction Models. Proceedings of the 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.21638\/spbu14.2023.413","article-title":"Development of Legal-Tech Prospects in the Federal Republic of Iraq: The Predictive Justice in Anglo-Saxon and Latin Perspectives","volume":"14","author":"Abdulkareem","year":"2023","journal-title":"Vestn. St. Petersburg Univ. Law"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.envsoft.2018.09.016","article-title":"Advances in Bayesian Network Modelling: Integration of Modelling Technologies","volume":"111","author":"Marcot","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1002\/int.20367","article-title":"From Dynamic Influence Nets to Dynamic Bayesian Networks: A Transformation Algorithm","volume":"24","author":"Haider","year":"2009","journal-title":"Int. J. Intell. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.fsigen.2017.12.006","article-title":"A Template for Constructing Bayesian Networks in Forensic Biology Cases When Considering Activity Level Propositions","volume":"33","author":"Taylor","year":"2018","journal-title":"Forensic Sci. Int. Genet."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pereira-Fari\u00f1a, M., and Bugar\u00edn-Diz, A. (2019, January 9\u201313). Content Determination for Natural Language Descriptions of Predictive Bayesian Networks. Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), Prague, Czech Republic.","DOI":"10.2991\/eusflat-19.2019.107"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"012023","DOI":"10.1088\/1742-6596\/490\/1\/012023","article-title":"An Intuitive Dashboard for Bayesian Network Inference","volume":"490","author":"Reddy","year":"2014","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cook, T., Gee, J.C., Bryan, R.N., Duda, J.T., Chen, P.-H., Botzolakis, E., Mohan, S., Rauschecker, A., Rudie, J., and Nasrallah, I. (2018, January 10\u201315). Bayesian Network Interface for Assisting Radiology Interpretation and Education. Proceedings of the Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, Houston, TX, USA.","DOI":"10.1117\/12.2293691"},{"key":"ref_32","first-page":"104658","article-title":"An Online Platform for Spatial and Iterative Modelling with Bayesian Networks. Environmental Modelling","volume":"127","author":"Stritih","year":"2020","journal-title":"Software"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mclachlan, S., Paterson, H., Dube, K., Kyrimi, E., Dementiev, E., Neil, M., Daley, B.J., Hitman, G.A., and Fenton, N.E. (December, January 30). Real-Time Online Probabilistic Medical Computation Using Bayesian Networks. Proceedings of the 2020 IEEE International Conference on Healthcare Informatics (ICHI), Oldenburg, Germany.","DOI":"10.1109\/ICHI48887.2020.9374378"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"347","DOI":"10.29109\/gujsc.929365","article-title":"Student Modelling on Language Teaching Based on Bayesian Networks","volume":"9","year":"2021","journal-title":"Gazi \u00dcniversitesi Fen Bilim. Derg. Part C Tasar\u0131m Ve Teknol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jongsawat, N., Poompuang, P., and Premchaiswadi, W. (2008, January 6\u20138). Dynamic Data Feed to Bayesian Network Model and SMILE Web Application. Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel\/Distributed Computing, Phuket, Thailand.","DOI":"10.1109\/SNPD.2008.67"},{"key":"ref_36","first-page":"1","article-title":"Potentials of Bayesian Networks to Deal with Uncertainty in Climate Change Adaptation Policies","volume":"70","author":"Catenacci","year":"2009","journal-title":"CMCC Res. Pap."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1118\/1.597400","article-title":"A Bayesian Network Model for Radiological Diagnosis and Procedure Selection: Work-up of Suspected Gallbladder Disease","volume":"21","author":"Haddawy","year":"1994","journal-title":"Med. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Isafiade, O.E., Bagula, A.B., and Berman, S. (2020). On the Use of Bayesian Network in Crime Suspect Modelling and Legal Decision Support. Advances in Data Mining and Database Management, IGI Global.","DOI":"10.4018\/978-1-7998-0951-7.ch019"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Codetta-Raiteri, D. (2021). Editorial for the Special Issue on \u201cBayesian Networks: Inference Algorithms, Applications, and Software Tools\u201d. Algorithms, 14.","DOI":"10.3390\/a14050138"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1613\/jair.4649","article-title":"Probabilistic Inference Techniques for Scalable Multiagent Decision Making","volume":"53","author":"Kumar","year":"2015","journal-title":"Jair"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1515\/ajle-2020-0034","article-title":"Machine Learning and Law and Economics: A Preliminary Overview","volume":"11","author":"Park","year":"2020","journal-title":"Asian J. Law Econ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1109\/TIE.2019.2907440","article-title":"Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty","volume":"67","author":"Peng","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_43","unstructured":"Bykov, K., H\u00f6hne, M.M.-C., Creosteanu, A., M\u00fcller, K.-R., Klauschen, F., Nakajima, S., and Kloft, M. (2021). Explaining Bayesian Neural Networks. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e37","DOI":"10.1002\/ail2.37","article-title":"Abstraction, Validation and Generalization for Explainable Artificial Intelligence","volume":"2","author":"Yang","year":"2021","journal-title":"Appl. AI Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Schwab, M., and Biswas, A.K. (2023, January 6\u20138). Invertible Neural Networks for Trustworthy AI. Proceedings of the 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), Atlanta, GA, USA.","DOI":"10.1109\/ICTAI59109.2023.00076"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1109\/TVCG.2021.3114864","article-title":"Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making","volume":"28","author":"Zytek","year":"2022","journal-title":"IEEE Trans. Visual Comput. Graph."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/2\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:36:26Z","timestamp":1760027786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/2\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":46,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["systems13020131"],"URL":"https:\/\/doi.org\/10.3390\/systems13020131","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,17]]}}}