AI in Forensic Science

By Dr. Surat P, Ph.D.Reviewed by Emily Henderson, B.Sc.

Machine learning and artificial intelligence (AI) are two emerging fields that are currently being applied to almost all areas of science. Due to recent advances in material sciences, the forensic field is now also benefitting from artificial intelligence.

How is AI aiding forensic science?

Data analysis

Digital forensics is an upcoming field that is high on computation and requires analysis of large and complex data sets. Here, AI provides a good tool to handle and resolve these large data sets.

For example, AI can be used to perform a meta-analysis of the meta-data derived from various sources, and pool them to simplify complex data. This can reduce the data of this form into a simplified and understandable format in a relatively short period of time.

Pattern recognition

Identifying specific types of patterns in large data is one of the crucial elements of forensic science. This may involve image pattern recognition where the software tries to identify different parts of an image or a person. Similarly, there may be other kinds of pattern recognition, such as detecting patterns in a text like email messages, or patterns in a sound file.

Pattern recognition is based on heavy statistics and probabilistic reasoning. AI can come to recognize such patterns in complex data in a more accurate manner. Some of the features may involve locating certain types of pictures or based on an understanding of how humans perceive information.

To achieve a high degree of success, pattern recognition methods should try to match against maximum possible data types. Practically, this is hard to achieve unless machine learning or AI methods are used. Using AI can also reduce the level of false-positive or false negatives in such analysis.

Providing legal solutions

Forensic statistics provides scientific methods to treat evidence for the legal system. With more complex and extensive information databases, AI can provide fast solutions to the legal community when required.

Improving communication between all members of the forensic team

Forensic investigation requires communication between forensic statisticians, lawyers, criminal investigators, and others. Mis-communication between these parties can lead to wrong decisions or misinterpretation of data leading to delayed or wrong justice. AI helps to bridge this communication gap between different partners in this field.

Building statistical evidence

Forensic science involves supporting the narrative and arguments with strong statistical evidence. AI can build graphical structures that can support building scenarios and case stories. It can also help build graphical model situations that can be used to prove or disprove arguments, helping the law to make better judgments.

AI provides mathematical and computational tools that can help to build statistically relevant and significant evidence. All this will reduce the errors and improve the understanding of the statistics behind a study.

Creating repositories

AI can also help to build an online repository that can store all the digital forensic investigations, data, properties, and results. With the exponential rate of development of storage capacity, such as USB, hard drives, optical media, flash drives that can store very large amounts of information, it is becoming harder for forensic science investigators to store and analyze all this data. AI can be a good tool to store, analyze and use this data for legal purposes.

Knowledge discovery

Data mining and knowledge discovery are other fields that require the use of AI. Datamining is a combination of AI, statistical analysis, and probabilistic methods that are all used together to collect and analyze large samples of data. Due to the enormous size of data, normal computational methods may not prove useful.

During data mining, the user may ask for certain files to be highlighted that contain specific information and relation to the user. This can aid in the process of pattern recognition. AI can also help avoid obvious patterns and focus on patterns of relevance.