When this has to be analyzed we need fast methods for selection of relevant data. For humans it would not be feasible to process these amounts of data manually, so machine learning is one of the options to solve this. As chair of forensic data science at the Institute for Informatics of the University of Amsterdam this is one of the topics of research. Also combined with Biometrics where the privacy protection are top priority this makes new solutions possible for law enforcement.
It is also expected that more relevant statistical information is deducted from the data. However as always most of the data is heterogeneous and might be contaminated, so before drawing conclusion one should know a measure of uncertainty of the data.
One of the issues with deep learning is that part of it is a black box, and methods to explain how the network learns from the training sets are under development. However at the other side the human brain of an expert can also be seen as a black box, since by visual comparison the expert also uses previous experiences and is sensitive to bias. Research in this field is conducted and should also provide solutions to cope with this bias within forensic science.
