The Potential of Machine Learning and Artificial Intelligence inForensicScience: An Overview of Key Applications, Challenges, andFutureDirections

Abstract

Forensic science has evolved signi𿿿cantly in recent years with the integrationof machine learning (ML) and arti𿿿cial intelligence (AI). The use of these technologies has created new possibilities and the ability to completely transform the industry. The ability to quickly and precisely analyze large volumes of data has signi𿿿cantlyimproved the effectiveness and accuracy of forensic analysis. In the context of arti𿿿cial intelligence and machine learning, this paper explores the functions of image analysis, pattern recognition, bloodstain pattern analysis, anomaly detection, cybersecurity, intrusion detection, age, sex, ancestry estimation, facial reconstruction, skeletal trauma analysis, DNA analysis, genomics, 𿿿ngerprint identi𿿿cation, and oice recognition. The function of numerous technologies, including virtual reality, neural networks, support vector machines, computer vision, CBR, and NLP, is also investigated. Despite the advantages, there are several drawbacks to using these technologies, including the requirement for high-quality data and the possibility of algorithmic bias. Therefore, it is imperative to provide moral and practical criteria for the application of AI and machine learning in forensic science. The main applications, dif𿿿culties, and potential future directions of machine learning and AI in orensics are outlined in this review paper. We aim to shed light on the possible advantages and dif𿿿culties of these technologies and provide insights into the future of forensic science by analyzing the state of the𿿿eld today