Owing to the fact that many legal questions can be framed as
binary classification problems, there are enormous possibilities
for applying predictive algorithms to the law. Building on recent
advances in machine learning, my colleagues and I at the University
of Toronto have created a system that analyzes patterns in the case
data for a range of tax law questions. As cases are inputted into
the system to form a data set, the machine learning develops an
algorithm that, after testing and calibration, can predict how the
courts are likely to rule in a new scenario. Each prediction is
also accompanied by a confidence level expressed as a
percentage.
Our system also allows us to observe how changing the fact
pattern of a case affects the probability of the outcome. In Part 2
of this series, we investigated how the presence of financial risk
factors significantly alters our algorithm's confidence in
determining whether financing is debt or equity. In Part 3, we
explored
