AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion Cases

Abstract

Since there are growing concerns regarding online privacy, firms may have the risk of being involved in various privacy infringement cases resulting in legal causations. If firms are aware of consequences from possible cases of invasion of online privacy, they can more actively prevent future online privacy infringements. Thus, this study attempts to predict the probability of judgment types caused by various invasions within US judicial cases that are related to online privacy invasions. Since legal judgment results are significantly influenced by societal factors and technological development, this study tries to identify a model that can accurately predict legal judgment with explainability. To archive the study objective, it compares the prediction performance by applying five types of classification algorithms (LDA, NNET, CART, SVM, and random forest) of machine learning. We also examined the relationship between privacy infringement factors and adjudications by applying network text analysis. The results indicate that firms could have a high possibility of both civil and criminal law responsibilities if they distributed malware or spyware, intentionally or non-intentionally, to collect unauthorized data. It addresses the needs of reflecting both quantitative and qualitative approach for establishing automatic legal systems for improving its accuracy based on the socio-technical perspective.