  Legal Judgment Prediction (LJP) is a longstanding and open topic in the
theory and practice-of-law. Predicting the nature and outcomes of judicial
matters is abundantly warranted, keenly sought, and vigorously pursued by those
within the legal industry and also by society as a whole. The tenuous act of
generating judicially laden predictions has been limited in utility and
exactitude, requiring further advancement. Various methods and techniques to
predict legal cases and judicial actions have emerged over time, especially
arising via the advent of computer-based modeling. There has been a wide range
of approaches attempted, including simple calculative methods to highly
sophisticated and complex statistical models. Artificial Intelligence (AI)
based approaches have also been increasingly utilized. In this paper, a review
of the literature encompassing Legal Judgment Prediction is undertaken, along
with innovatively proposing that the advent of AI Legal Reasoning (AILR) will
have a pronounced impact on how LJP is performed and its predictive accuracy.
Legal Judgment Prediction is particularly examined using the Levels of Autonomy
(LoA) of AI Legal Reasoning, plus, other considerations are explored including
LJP probabilistic tendencies, biases handling, actor predictors, transparency,
judicial reliance, legal case outcomes, and other crucial elements entailing
the overarching legal judicial milieu.
