## 2 Background and Related Work
### Deep Learning and Interpretability: Application to Litigation Analysis and Prediction
The SHAP (SHapley Additive exPlanations) method can also be used to compute importance scores using a game theoretic approach [76]. Deep learning, by contrast, relies on neural networks which are not readily interpretable. Methods have been developed to examine neural network structure, through relevance propagation, sensitivity analysis, and decomposition methods for the network [77]. Another approach is propagating activation differences through a neural network [78]. Saliency maps have been proposed in CNN as a way to identify important regions in visual regions and then applied to regions in text [79]. Another method for interpretability is to do backtracking through a CNN to find important text [80]. While the aforementioned methods involve analyzing neural network structure, another approach is to add attention: a neural network architecture designed for sequence prediction tasks, which can both learn and can be directly examined to identify key features in text. Classification may, for example, be done by a neural network that includes an attention layer after bidirectional-LSTM [81]. It has been shown that attention layer weights may be identified and used to highlight words using an attention encoder to summarize sentences [82]. A hierarchical architecture has also been developed employing attention with RNN to identify attention at the word, sentence, and paragraph level [60]. An important caveat is that while attention may identify features that were of particular importance when neural networks were learned in training, these may not be the most quantitatively significant features [83]. Empirical work suggests that attention can identify the actual important features in a classification task when attention is included as part of the development of the classifier, rather than serving merely as a window into the weights that are learned by the classifier [84]. In the legal domain, one work applied this method to U.S. Bureau of Veterans Appeals (BVA) decisions, but found that it was not scalable given longer text, and was very slow, reflecting the limitations of RNN architecture. It was therefore complemented by a semi-supervised approach focused on deriving text annotations that could help human interpretation of decisions [14]. Similar inaccuracy and scalability issues were found for BVA decisions when using CNN [16]. Another approach that was developed for medical coding is using attention on top of CNN to derive a per-label multilabel attention [85]. In one recent comprehensive study, interpretable machine learning using attention with RNNs, the hierarchical CNN with attention, multilabel attention, and BERT has also been demonstrated for judgment prediction in ECHR cases [86]. The authors found good classification performance. Notably, inferior performance was obtained using BERT when masking proper nouns, which suggested that BERT performed best when it overfit for the names of parties and other entities who had repeated patterns in cases. In this paper, we employ a neural network that combines a CNN, with multiple attention layers for interpretability, followed by a dense classification layer. CNN was presented as a method to analyze text by Microsoft in an application for web search [87]. Our CNN method is based on a method for sentence classification presented in [88]. We employ CNN instead of RNN in part because CNN is much faster on modern hardware architectures, such as GPU and Google's specialized Tensor Processing Unit (TPU), as well as the method's tolerance for large text without over-consuming memory resources (a problem with transformer-based methods, which we evaluated but do not present in this work) [89]. It has also been suggested that CNNs may outperform RNNs in sequence modeling tasks even when memory is not at issue [90]. We obtain model interpretability by highlight key words using attention values, following [82].