{'fraction_non_alphanumeric': 0.01072961373390558, 'fraction_numerical': 0.0, 'mean_word_length': 5.390410958904109, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fair Use Defences During Copyright Litigation

The prediction of legal outcomes and other legal domain related variables has served as the basis of a number of recent studies. While recent studies have estimated standardised variables and dichotomous outcomes such as the outcome of a judicial decision process, few studies have employed dichotomous data and categorical data to predict the basis of a legal defense strategy or the likelihood of trial success. Empirical research within the judicial sciences continues to employ a limited subset of empirical methods. This article reasserts the benefits of several artificial intelligence based non-parametric techniques that are better suited to the discipline than many of the common methods employed within the literature. The article considers the predictability of fair use defense within the U.S. during copyright infringement proceedings, and the likelihood of trial success.