The generative artificial intelligence represented by Sora and ChatGPT has been further integrated with criminal justice, and exploring the deep participation of artificial intelligence in the field of forensic appraisal has achieved initial results, while new problems have been exposed. Traditional appraisal opinions generation model with expert and experience as the center is susceptible to cognitive bias, and algorithms and data as the center of artificial intelligence participation in the generation of appraisal opinions can effectively overcome the cognitive bias. With artificial intelligence acting as an examiner and the appraiser as a “situational manager”,  the reliability of appraisal opinions and the efficiency of forensic appraisal will be enhanced. It’s necessary to prevent the possible new problems, such as “algorithm black box” and “algorithmic bias”, in the application of generative artificial intelligence in forensic appraisal, and to prevent falling into the “new doctrine legal evidence” of the digital age. Firstly, the man-machine relationship of “the appraiser as the main body, and the machine as the auxiliary”should be established. It is necessary to clarify that the appraiser is responsible for the review and supervision of the algorithm’s participation in the generation of appraisal opinions. Secondly,  a data specification and traceability mechanism should be established to prevent the “source pollution” of the algorithmic model learning content. Finally, as to the disclosure and effective review of the algorithms, a feasible solution is to build an independent algorithm review committee and an algorithm review hearing procedure, in order to cushion the conflict between the requirement for public scrutiny of algorithms and the protection of commercial interests.

With the rapid development of the information technology, artificial intelligence has empowered various industries. Forensic appraisal industry is not an exception, which is undergoing significant and rapid changes. To take advantages of technological characteristics of artificial intelligence fully, and apply it to forensic appraisal with a better, standardized and efficient way is a continuously-discussed topic among professional people. Firstly this article introduces basic concept and development history of artificial intelligence technology and describes its impact towards traditional forensic appraisal briefly, as well as the policy innovations and changes made by judicial authorities to face and adapt to new challenges. Secondly, an in-depth discussion about opportunities and assistance brought by artificial intelligence is proposed from multiple perspectives, including appraisal technology and auxiliary procedures, starting from the forensic appraisal in the “Four Categories”. Finally, the article analyzes issues and challenges in depth which forensic appraisal may encounter from various aspects such as application challenges and practical difficulties. A summary and prospect are put forward on the combination of artificial intelligence and forensic appraisal, with a positive attitude towards its development future.

With the rapid development of the intelligent and connected vehicles industry, the accident investigation of intelligent and connected vehicles has attracted general attentions of local governments and authorities in China. Firstly, from the perspective of traffic accident analysis, the paper first analyzed the multi-source heterogeneous information prevalent in intelligent and connected vehicles, in terms of the excecution standards, functions authentication, scope of models, storage location and access to typical information. Secondly, combined with the rules of investigation and evidence collection in road traffic accidents and the evidence requirements of appraisal opinions, the typical information involved in intelligent and connected vehicles accidents was analyzed for traceability and evidence effectiveness. Thirdly, according to the purpose of intelligent and connected vehicles accidents investigation and appraisal  requirements, combined with the multi-source heterogeneous characteristics of acquired information, the calibration and fusion of multi-modal information was proposed. Finally, an outlook on accident investigation and identification techniques for intelligent and connected vehicles was presented.

The development of China’s new energy vehicles industry has achieved remarkable results. On the other hand, the characteristics of new energy vehicles accidents such as unexpected acceleration, power interruption or outbreak of a fire, have become new difficulties and challenges for accident investigation and responsibility identification. This paper systematically analyzed the current problems of accident investigation and evidence collection in new energy vehicles accidents, as well as the application of accident investigation based on remote supervision data. Accordingly, we proposed the concept, plan and work suggestions for building a third party forensics platform for collecting electronic data of new energy vehicles, aiming at providing feasible solutions for related accident investigation and evidence collection.

Taking high-speed rear end collision of intelligent and connected vehicles as examples, the existing requirements for recording vehicle data information were introduced in this paper, around the data information required for accident process and accident cause analysis. Based on the current status of testing and evaluation of intelligent and connected vehicles, this paper analyzed the causes of seven intelligent connected vehicles traffic accidents, including the research institutions and their capabilities on the investigation of such traffic accidents, and the disclosure of data on these seven intelligent and connected vehicles traffic accidents. With the challenges faced by multi-sensor fusion perception technology, the lack of standards for recording sensor perception data, and the scarcity of publicly available traffic accident data information in China, the idea of establishing a cross disciplinary accident research institution has been proposed.

With the rapid development of China’s automobile industry, the penetration rate of L2 level passenger cars has increased rapidly. The traffic accidents identification of intelligent vehicles is more complicated than that of traditional vehicles, which brings new challenges to traffic accidents investigation. Combined with the sequential process of traffic accidents, this paper proposed a data driven identification method for intelligent vehicles traffic accidents. Based on accident related data, the method integrates multi-source heterogeneous data that were recorded during the accident process of intelligent vehicles to analyze the cause of the accidents and the safety of intelligent vehicles. Additionally, a typical case was discussed as an example of intelligent vehicles traffic accidents investigation and identification.

Blood stain is the trace evidence with the highest frequency of encountered and the richest information, and it is the key evidence needed to solve the case. By observing and analyzing the physical form and chemical characteristics of the blood stain left on the scene, investigators can partially restore the criminal process and tools, which provides an irreplaceable role for narrowing the scope of investigation and analyzing the case. The characteristics of the blood stains on different tissue carriers will change regularly with the passage of time, and even at the same exposure time, the blood stains on different carriers have different manifestations in different dilution methods and concentrations. Therefore, the detection of blood stains, the discrimination between blood stains and other body fluids, and the prediction and inference of the formation time of blood stains have always been extremely important research directions in case investigation. In this paper, the research progress of blood trace detection based on direct observation, biochemical method, optical visualization method, colorimetric quantification method and so on in recent years is reviewed, in order to provide reference for forensic practice and research.

Since the sewage analysis method was proposed, its role in the field of illicit drugs monitoring cannot be ignored. However, with the deepening and expansion of its application, some common problems that urgently need to be studied and solved have been found in the practical application. This study focuses on common issues and relevant literature reports in recent years to analyze and explore factors that may cause deviations in illicit drugs monitoring and evaluation results throughout the entire process of sewage analysis. It includes sample collection, target substance stability, sample pretreatment, quantitative analysis and detection, metabolic target substance selection, and correction factor calculation. At the same time, the future research direction of sewage analysis method was prospected, so that sewage analysis method could better serve the illicit drugs monitoring work, and provide reference for the accuracy of source tracking and related theoretical research.

Objective To explore different calculation strategies for the likelihood ratio (LR) identity identification of tri-allelic pattern short tandem repeat (STR) loci. Methods Calculated the LR of tri-allelic pattern loci by ignoring the tri-allelic pattern loci, based on observations values in population, and based on the formula derivated from the mechanism of tri-allelic pattern loci. Results The LR calculation methods for three models and six strategies were obtained, and their conservatism and limitations were analyzed. Conclusion The LR calculation formula for tri-allelic pattern loci can be derived based on the mechanism of trisomy syndrome, enabling the acquisition of more accurate LR values applicable in forensic appraisal.

Objective In view of the limitations of optical coherence tomography (OCT) technology in the process of document inspection, a new numerical algorithm is developed from a deeper level, so as to realize the quantitative processing of OCT data set, and to provide supplementary evidence for document identification. Methods In this study, a frequency-domain OCT system with a central wavelength of 905 nm was used for in situ nondestructive testing of different printing ink and handwriting samples. Matlab programming software was also used to develop a set of numerical algorithms to perform a series of numerical analyses on OCT data set. In addition to applying the calculated results to the basic tomography visualization, the ink-paper interfaces were also identified accurately. Moreover, based on the established three-dimensional coordinate system, the surface average intensity (SAI) in different depth directions were calculated. Results By comparing SAI profiles of 14 handwriting samples, the different printing ink and handwritten handwriting were successfully distinguished. The SAI profiles of laser printing ink and pigment ink handwriting have an extreme value, while the SAI profiles of inkjet printing ink and dye ink handwriting were smoother. Conclusion Based on the quantitative analysis of OCT technology, it can provide an effective method for document identification. The OCT characterization results have a certain guiding role, and can lay a foundation for enriching the existing ecosystem of measurement techniques.

Objective The aim is to investigate the impact of the quality and image restoration methods of small face sample on the performance of facial recognition systems, in order to improve the effectiveness of forensic appraisal recognition. Methods This paper focused on the impact of few-shot face image inpainting on face recognition technology in natural scenes. We selected few-shot face images from the Labeled Faces in the Wild database. Then, we used Gaussian filtering technology on these images. This simulated the quality of real-life blurred face images. At the same time, different face image inpainting methods based on deep neural networks were used to repair the blurred face images. The influence of different few-shot face images quality and different inpainting methods on the recognition performance of face recognition was studied by statistical analysis. Results The face image inpainting technology enhanced the clarity of small face image samples. Moreover, the technology boosted the visual recognition of identity information in these images. However, the repaired face images negatively affected face recognition. Conclusion Face image inpainting technology plays a positive role in portrait identification, but it should be used more carefully when be applied in face recognition.

In view of the dilemma often encountered in the practice of forensic psychiatry appraisal, this paper reviews the historical context of forensic psychiatry, sorts out and clarifies the role and responsibility of forensic psychiatrists, which is quite different from clinical psychiatrists, and makes a preliminary discussion on the value orientation and ethical principles in forensic psychiatric practice. On the premise of acknowledging the basic scientific attributes of forensic psychiatry, exploring the truth should be the first value pursuit. Besides, considering the interface of law and psychiatry, seeking both truth and goodness should be established as the overall value orientation for forensic psychiatrists. The whole process of forensic psychiatry appraisal should be checked by a specific set of ethical principles, and the optimal solution should be determined within a framework of dialectical principlism.

Expert jurors in environmental public interest litigation present the following practical characteristics: professional of environmental rights and interests relief, all-round protection of public interests, comprehensive realization of judicial justice. In the existing practice of expert jurors, the selection, work and job security of the corresponding personnel have not received due response from the reformers, and there are problems such as insufficient specificity of the legal basis, deviation of the selection method from the needs, and lack of relationship coordination. Taking into account the useful practice of various countries in terms of normative basis, personnel selection and structural arrangement, and responsibility allocation, reformers should form a legal norm system covering the organization, work and security of expert jurors, build a personnel selection mechanism that combines recommendation and selection, and develop the support force for different organizations to accept expert jurors from the perspective of concept and practice.

As the “king of evidence” in the era of network information, electronic data carries case facts that can solve specialized issues in criminal disputes. In order to maximize the positive role of electronic data in case fact determination and promote the transformation of digital forensics from scientific fact determination to legal fact determination, it is necessary to sort out and analyze potential risk factors that affect digital forensics. Based on the hazard analysis method, a multidimensional and multi factor risk analysis is conducted on the three risk factors that affect the quality and effectiveness of digital forensics, including digital forensics authenticator, digital forensics procedures and methods, and digital forensics operating environment. In the risk assessment process, the connotation and composition of digital forensics risks were preliminarily categorized, providing useful insights for quality management of digital forensics.