The aim of our project is to train police officers’ on the procedure, through gamification technologies in a safe and controlled virtual environment. Essential tasks during the creation of LAW-GAME serious game are to virtualise and accurately recreate the real world. We will introduce an attractive approach to the development of core competencies required for performing intelligence analysis, through a series of AI-assisted procedures for crime analysis and prediction of illegal acts, within the LAW-GAME game realm. Building upon an in-depth analysis of police officers’ learning needs, we will develop an advanced learning experience, embedded into 3 comprehensive “gaming modes” dedicated to train police officers and measure their proficiency in: 1. conducting forensic examination, through a one-player or multi-player cooperative gaming scenario, played through the role of a forensics expert. Developed AI tools for evidence recognition and CSI and car accident analysis, will provide guidance to the trainee. 2. effective questioning, threatening, cajoling, persuasion, or negotiation. The trainee will be exposed to the challenges of the police interview tactics and trained to increase her emotional intelligence by interviewing a highly-realistic 3D digital character, advanced with conversational AI. 3. recognizing and mitigating potential terrorist attacks. The trainees will impersonate an intelligence analyst tasked with preventing an impending terrorist attack under a didactic and exciting “bad and good” multiplayer and AI-assisted game experience. The proposed learning experience focuses on the development of the key competences needed for successfully operating in diverse and distributed teams, as required by several cross-organisational and international cooperation situations. The learning methodology developed by the LAW-GAME consortium will be extensively validated by European end-users, in Greece, Lithuania, Romania, Moldavia and Estonia.

Transfer traces analysis currently suffers from several limitations: highly subjective and selective, this process is also very time-consuming, hence inefficient, whereas results are difficult, if not impossible, to compare and share among forensic laboratories. As a consequence, the use of forensic data in cross-border investigations, and in foreign courts is limited. Creating a unified transfer traces automated analysis toolkit would allow for a higher productivity of forensic workers, better crime resolution and enable further collaboration across end-users. In addition to the lack of maturity of its potential components, the economical question is the barrier that hinders the development of such a toolkit as a sole forensic service can’t afford funding the corresponding R&D activities. the SHUTTLE project then intends to run a Pre-Commercial Procurement (PCP) action between forensic institutes across Europe to mitigate these technical and financial barriers and jointly carry out the procurement of the necessary Research and Development (R&D) activities to develop a machine+toolkit that will integrate different tape analysis tools to automate the routine part of the work of trace evidence examiners and, eventually, strengthen further judicial and police cooperation. The SHUTTLE toolkit will be scientific, as the results obtained will be objective and validated, which will strengthen their usefulness and usability. It will be high-throughput, as the instrumentation will be built to process large amounts of samples and data. The acquired data will be stored in a database together with the results of other methods. These databases can be shared and maintained together, and be used to provide a scientifically justified, numerical evidential value. Finally, SHUTTLE, which will be validated in conformity with ISO17025, will unify the methodology used in several European countries and will foster collaboration across countries and institutions in Europe.

Discovering criminal networks and identifying their members is one of the primary aspects of LEAs' mission. ROXANNE will contribute towards this goal by bridging the strengths of speech and language technologies (SLTs), visual analysis (VA) and network analysis (NA). If funded, ROXANNE will achieve a significant increase in the speed of investigation processes and an improvement in identification of individuals by means of speech, in the scope of criminal cases where large amounts of lawfully intercepted communications (with multilingual attributes) are analysed. The technical development will be centred around the ROXANNE platform, which will enhance criminal network analysis capabilities by providing a framework for extracting evidence and actionable intelligence based on speech, language and video technologies. The intention is not to replace humans but automate time-consuming tasks, and support LEA decision-making. Its early version will offer preliminary SLT, VA and NA capabilities to collect end-user feedback. The final version will provide multilingual, probabilistic tools interfacing SLT and NA technologies, boosted by natural language processing (NLP) and relation analysis in the synoptic criminal activity graph. ROXANNE will achieve full compliance with relevant INTERPOL and EU legal and ethical frameworks, including innovative approaches to data protection management such as privacy by design. Special efforts will be expended to ensure ROXANNE outcomes achieve widespread adoption by law enforcement. The effort will be enhanced through a series of education and awareness campaigns and the direct involvement of LEAs from nine European countries, that will test our solutions on real case data. In addition, ROXANNE partner INTERPOL and EUROPOL (member of the External Advisory Board) will provide advice and guidance. The consortium has 24 partners with complementary skills, including leaders in key technology areas impacting criminal investigations.