This chapter reviews the use of data science and machine learning
applications to facilitate the analysis of evidence and the processes of juridical proof.
Innovations in data science and machine learning have led to advancements in forensic
interpretation, risk prediction, and the efficient processing of large datasets of ‘open
source’ material. However, the ‘holy grail’ of data science development in the evidence
domain is the modelling of legal arguments themselves. Data science holds the
potential to play a leading role in understanding and modelling the fact-finding process.
Argumentation schemes have thus become a major research focus and hold the
potential - when aligned with parallel developments in legal reasoning – to fully
automate the trial process. This chapter considers the standard applications of data
science to legal evidence – forensics, prediction, and data processing - before
reviewing the work of the Groningen School, which has played an important role in
shaping this field. Crucially, it is intended that this introductory chapter offer an
integrated perspective on evidential reasoning, demonstrating not only the unique
affordances of particular inferential modes encountered in the surveyed literature but
also highlighting the compound and contextually dependent nature of these features.
This chapter, therefore, highlights the importance of attention to conditioning factors
based upon the structure of, and inter-relations between, the overall form of the legal
system, legal sub-field, mode of argumentation, and form of adjudication.
Keywords: Artificial intelligence, Argumentation theory, Law, Machine learning, Philosophy of evidence.
