Project Description

A self-learning system analyzes documents combining both optical/image and textual characteristics in order to simulate the human interpretative abilities.

Investigators in (criminal) prosecution have to sift through, inspect and evaluate huge amounts of heterogeneous data and documents – evidence seized while searching the homes or office premises of suspects. Due to the ever increasing amount of data used, stored and communicated daily, the need for computational means of analytical support is pressing. However, computational tools for textual analysis completely lack the intuitive ability of drawing conclusions from the structure of documents, optical characteristics, image elements (like logos, stamps, or written additions) and their positions, that comes naturally to humans. This inadequacy becomes even more pronounced considering the often defective text produced by OCR-software on documents of bad scan-quality.

K.Rex proposes an approach to capture elements of human perception in machine learned models to facilitate the interpretation of documents that otherwise cannot be considered in computer aided investigations and hence are lost from the chain of evidence. Methods for analysing text and images are combined in a multimodal system in order to increase the accuracy and pertinence of the computationally derived (possible) facts. In order to train the components of the system expert knowledge is efficiently recorded and formalised, specifically accounting for the idiosyncratic intricacies of each case and the ever changing patterns of fraud. Techniques for adaptive, dynamic learning will be explored to ensure fast and straightforward adaptation to new requirements with minimal effort for additional manual annotation.

The socio-technical implications of computer-aided investigations are substantial. Aspects of trust, accountability and transparency will be discussed and an ethical framework established. Early integration of sociological expertise can counteract and prevent effects like cognitive bias, preconceptions, or prejudice unintentionally instigated by the software. Iterative evaluation in a laboratory environment throughout the entire process ensures that the research meets the demands and expectations of the target audience.

Due to the expected reduction in turnaround times for the prosecution while improving the quality of the investigative results at the same time, K.Rex has the potential to enhance the performance and efficiency of future analysis tools significantly.

Contact: katja.mayer@univie.ac.at

Project Coordinator:
Doris Ipsmiller, m2n – consulting and development gmbh
Götzwiesenstraße / Knagg 6, 3033 Maria Anzbach
Tel: +43 660 7119870
eMail: ipsmiller@m2n.at 
Web: www.m2n.at

Cooperating Partners: 

• Federal Ministry of Finance
• Federal Ministry of the Interior
• Federal Ministry of Constitutional Affairs, Reforms, Deregulation and Justice
• Financial Market Authority
• Research Studios Austria Forschungsgesellschaft mbH
• TU Wien, Institute of Information Systems Engineering
• University of Vienna, Department of Science and Technology Studies