Developing an Explainable AI System for Digital Forensics: Enhancing Trust and Transparency in Flagging Events for Legal Evidence
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Abstract
Advanced forensic approaches are necessary to handle digital crimes, as they must provide transparent methods that foster trust and enable interpretable evidence in judicial investigations. The current black-box machine learning models deployed in traditional digital forensics tools accomplish their tasks effectively yet fail to meet legal standards for admission in court because they lack proper explainability.
This study creates an Explainable Artificial Intelligence (XAI) system for digital forensics to improve flagging events as legal evidence by establishing high levels of trust and transparency. A digital evidence system employs interpretable machine learning models together with investigative analysis techniques for the detection and classification of computer-based irregularities, which generate clear explanations of the observed anomalies.
The system employs three techniques, including SHAP (Shapley Additive Explanations) alongside LIME (Local Interpretable Model-agnostic Explanations) and counterfactual reasoning to deliver understandable explanations about forensic findings, thus enhancing investigation clarity for law enforcement agents and attorneys as well as stakeholder professionals.
The system performs successfully on actual digital forensic datasets, thus boosting investigation speed while minimizing false alerts and improving forensic decision explanations. The system must demonstrate GDPR and digital evidence admission framework compliance to maintain legal and ethical correctness for usage in court procedures.
Forensic digital investigations need explainable Artificial Intelligence as an essential integration for creating reliable and legally sound practices.
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Copyright (c) 2025 Billah M.

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Jarrett A, Choo KKR. The impact of automation and artificial intelligence on digital forensics. Wiley Interdiscip Rev Forensic Sci. 2021;3(6):e1418. Available from: http://dx.doi.org/10.1002/wfs2.1418
Sarker IH. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Comput Sci. 2022;3(2):158. Available from: https://link.springer.com/article/10.1007/s42979-022-01043-x
Ypma RJ, Ramos D, Meuwly D. AI-based forensic evaluation in court: The desirability of explanation and the necessity of validation. Artif Intell Forensic Sci. 2023;2.
Solanke AA. Explainable digital forensics AI: Towards mitigating distrust in AI-based digital forensics analysis using interpretable models. Forensic Sci Int Digit Investig. 2022;42:301403. Available from: https://doi.org/10.1016/j.fsidi.2022.301403
Shamoo Y. The Role of Explainable AI (XAI) in Forensic Investigations. In: Digital Forensics in the Age of AI. IGI Global Scientific Publishing; 2025;31–62. Available from: https://www.igi-global.com/chapter/the-role-of-explainable-ai-xai-in-forensic-investigations/367310
Hall SW, Sakzad A, Minagar S. A proof of concept implementation of explainable artificial intelligence (XAI) in digital forensics. In: Int. Conf. Netw. Syst. Secur. Cham: Springer; 2022;66–85. Available from: https://research.monash.edu/en/publications/a-proof-ofconcept-implementation-ofexplainable-artificial-intelli
Arthanari A, Raj SS, Ravindran V. A Narrative Review in Application of Artificial Intelligence in Forensic Science: Enhancing Accuracy in Crime Scene Analysis and Evidence Interpretation. J Int Oral Health. 2025;17(1):15–22. Available from: https://journals.lww.com/jioh/fulltext/2025/01000/a_narrative_review_in_application_of_artificial.2.aspx
Díaz-Rodríguez N, Del Ser J, Coeckelbergh M, López de Prado M, Herrera-Viedma E, Herrera F. Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Inf Fusion. 2023;99:101896. Available from: https://doi.org/10.1016/j.inffus.2023.101896
de Filippis R, Al Foysal A. Integrating Explainable Artificial Intelligence (XAI) in Forensic Psychiatry: Opportunities and Challenges. Open Access Libr J. 2024;11(12):1–19. Available from: https://doi.org/10.4236/oalib.1112518
Rai Y, Saritha SK, Roy BN. Interpreting machine learning models using model-agnostic approach. In: AIP Conf. Proc. 2023;2745(1). Available from: https://ui.adsabs.harvard.edu/link_gateway/2023AIPC.2745b0015R/doi:10.1063/5.0143186
Kloosterman A, Mapes A, Geradts Z, van Eijk E, Koper C, van den Berg J, et al. The interface between forensic science and technology: how technology could cause a paradigm shift in the role of forensic institutes in the criminal justice system. Philos Trans R Soc B Biol Sci. 2015;370(1674):20140264. Available from: https://doi.org/10.1098/rstb.2014.0264
Hariharan S, Velicheti A, A.S. A, Thomas C, Balakrishnan N. Explainable artificial intelligence in cybersecurity: A brief review. In: Proc. 2021 4th Int Conf Secur Priv (ISEA-ISAP). 2021;1–12. Available from: http://dx.doi.org/10.1109/ISEA-ISAP54304.2021.9689765
Zhang J, Lei Y. Trend and Identification Analysis of Anti‐investigation Behaviour in Crime by Machine Learning Fusion Algorithm. Wirel Commun Mob Comput. 2022;2022(1):1761154. Available from: https://doi.org/10.1155/2022/1761154
Costantini S, De Gaspers G, Olivieri R. Digital forensics and investigations meet artificial intelligence. Ann Math Artif Intell. 2019;86(1):193–229. Available from: https://link.springer.com/article/10.1007/s10472-019-09632-y
Charmat F, Tanuwidjaja HC, Ayoubi S, Gimenez P-F, Han Y, Jmila H, et al. Explainable artificial intelligence for cybersecurity: a literature survey. Ann Telecommun. 2022;77(11):789–812. Available from: http://dx.doi.org/10.1007/s12243-022-00926-7
Rajapaksha S, Kalutarage H, Al-Kadri MO, Petrovski A, Madzudzo G, Cheah M. AI-based intrusion detection systems for in-vehicle networks: A survey. ACM Comput Surv. 2023;55(11):1–40. Available from: https://doi.org/10.1145/3570954
Ibrahim S, Nazir S, Velastin SA. Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis. J Imaging. 2021;7(11):225. Available from: https://doi.org/10.3390/jimaging7110225
Ding H, Chen L, Dong L, Fu Z, Cui X. Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection. Future Gener Comput Syst. 2022;131:240–54. Available from: https://doi.org/10.1016/j.future.2022.01.026
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57. Available from: https://doi.org/10.1613/jair.953
Alsubaei FS, Almazroi AA, Ayub N. Enhancing phishing detection: A novel hybrid deep learning framework for cybercrime forensics. IEEE Access. 2024;12:8373–89. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10384876
Wu J. Introduction to convolutional neural networks. Natl. Key Lab Nov. Softw. Technol., Nanjing Univ., China. 2017;5(23):495. Available from: https://cs.nju.edu.cn/wujx/paper/CNN.pdf
Nanduri A, Sherry L. Anomaly detection in aircraft data using Recurrent Neural Networks (RNN). In: 2016 Integr. Commun. Navig. Surveill. (ICNS). Apr 2016;5C2-1. Available from: https://catsr.vse.gmu.edu/pubs/ICNS_2016_AnomalyDetectionRNN_01042015.pdf
Rimal Y, Sharma N, Alsadoon A. The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms. Multimed Tools Appl. 2024;83(30):74349–64. Available from: http://dx.doi.org/10.1007/s11042-024-18426-2
Tyagi AK, Kumari S, Richa. Artificial Intelligence‐Based Cyber Security and Digital Forensics: A Review. In: Artif. Intell.‐Enabled Digit. Twin Smart Manuf. 2024;391–419. Available from: http://dx.doi.org/10.1002/9781394303601.ch18
Donald A, Iqbal J. Implementing Cyber Defense Strategies: Evolutionary Algorithms, Cyber Forensics, and AI-Driven Soluti©ons for Enhanced Security.
Tripathy SS, Behera B. Evaluation of future perspectives on Snort and Wireshark as tools and techniques for intrusion detection systems. SSRN. 2024;5048278. Available from: https://dx.doi.org/10.2139/ssrn.5048278
Chen T. Xgboost: extreme gradient boosting. R Packag. Version. 2015;0.4-2(1):1-4.
Ch R, Gadepalli TR, Abidi MH, Al-Ahmari A. Computational system to classify cybercrime offenses using machine learning. Sustainability. 2020;12(10):4087. Available from: https://doi.org/10.3390/su12104087