Hardly any field of expertise possess, produces and analyses such amount of content diverse information as law. In
the past decade, we have witnessed accelerated digitalisation of different legal
sources. Not only that digitalisation enabled and shortened access time to increased range of its users,
but it also enabled exponential
development of interdisciplinary legal field called legal analytics. As a scientific discipline,
legal analytics studies data patterns, data connections
and data relations among various legal sources. Legal analytics deals with specific nature of legal data
and examines methods of legal data analysis.
Moreover, legal analytics is tightly connected with computer science on one the hand and legal argumentation theory on the other. Described data analysis, where
machines provide computational examination of data which aims to uncover data connections in large data sets with purpose
to solve un- known cases
is called machine
learning. The purpose
of divergent computati- onal supported data analysis is to obtain
data structures either
for further data management (interim
solution) or final contribution to certain legal solution. The utmost goal of such legal data
analysis is either content based legal solu-
tion (e.g. predicting court decisions) or legal process
optimisation (e.g. acqui-
ring electronic statement from land registry). In the first part of the
article, the author explains basic
elements of machine learning, followed by the part dealing with practical
aspects of machine
supported legal problem-solving.
Key words: legal analytics, data analysis, machine learning, machine learning models,
law.