Machine Learning: The key to understanding the decision making of the US Supreme Court?

Abstrakt

The US Supreme Court enjoys power and authority unseen in any other jurisdiction in the world, which causes speculation about the basis for its decision-making and the factors influencing the decisions arrived at by the Court. Its structure, while ostensibly demonstrating clear independence, can be subject to misuse. Its decision making process is so complex that a human being, even an expert, can have difficulties understanding it. The Supreme Court provides thousands of documents containing information about each of the cases it decides upon and other relevant information, which can be analysed with machine learning methods to provide insight into the patterns, outliers and the most significant factors in the decision making of the justices, which helps to more fully understand their decision making. This thesis seeks to remove the ‘blackbox’ of decision making of the US Supreme Court, and to present Machine Learning not only as a tool to understand the Court, but also as a solution to monitor potential bias in its decisions. This is a great opportunity for businesses, where Supreme Court rulings can have a major impact on their bottom line, to better help them assess every potential threat and opportunity.