Unpacking a model: An interactive visualization of a text similarity algorithm for legal documents

Abstract

This paper presents a functional prototype for an interactive web-based interface i_sift developed to foreground the decision-making process of an algorithm that detects similarities in legal texts through word embeddings. Using this as a case study in Computational Social Science, our goal is, first, to highlight the importance of making computational tools and methods transparent to social scientists. Secondly, we suggest an approach that accomplishes this using methods and principles from Interactive Machine Learning and the Algorithmic Experience framework.