De-Biased Court’s View Generation with Causality (2020)

Abstract: Court’s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation. While prior text-to-text natural language generation (NLG) approaches can be used to address this problem, neglecting the confounding bias from the data generation mechanism can limit the model performance, and the bias may pollute the learning outcomes. In this paper, we propose a novel Attentional and Counterfactual based Natural Language Generation (AC-NLG) method, consisting of an attentional encoder and a pair of innovative counterfactual decoders. The attentional encoder leverages the plaintiff’s claim and fact description as input to learn a claim-aware encoder from which the claim-related information in fact description can be emphasized. The counterfactual decoders are employed to eliminate the confounding bias in data and generate judgment-discriminative court’s views (both supportive and non-supportive views) by incorporating with a synergistic judgment predictive model. Comprehensive experiments show the effectiveness of our method under both quantitative and qualitative evaluation metrics.

## introduction owing to the prosperity of machine learning, especially the natural language processing (nlp) techniques, many legal assistant systems have been proposed to improve the effectiveness and efficiency of a judge from different aspects, such as relevant case retrieval @xcite , applicable law articles recommendation @xcite , controversy focus mining @xcite , and judgment prediction @xcite @xcite @xcite ., * corresponding authors. ## problem formulation in this work, we focus on the problem of the court's view generation in civil cases, where the input is the plaintiff's claim and the fact description, and the output is the corresponding court's view. we formulate our problem with the definition of the plaintiff's claim, the fact description, and the court's view, as shown in fig. 2 . plaintiff's claim (c) is a descriptive sentence that depicts the claims from the plaintiff. in a civil case, it often appears multiple claims from the figure 3: the architecture of ac-nlg, which consists of a claim-aware encoder, a pair of counterfactual decoders, and a judgment predictor. fact description (f) is also a descriptive sentence, which describes the identified facts (relevant events that have happened) in a case, as fig. 2 shows. here, we denote the fact description in a case as @xmath0, where n is the length. court's view (v) contains two main components, judgment and rationales, where the judgment is to respond the plaintiff's claims, and the rationales are the claim-related summarization on the fact description to determine and interpret the judgment. here, we denote the court's view as @xmath1, where l is the length. moreover, we use a variable j to denote the judgment in the court's view. for simplicity, we set @xmath2, and @xmath3. then, the problem of court's view generation can be denoted as follow: ## method in this section, we first introduce the effect of mechanism confounding bias on the court's view generation and propose a backdoor-inspired method to eliminate that bias. then, we describe our attentional and counterfactual based natural language generation (ac-nlg) model in detail. fig. 3 shows the overall framework. ## ethical discussion while ai is gaining adoption in legal justice @xcite @xcite @xcite , any subtle statistical miscalculation may trigger serious consequences. from a fairness perspective, prior studies suggested that global (statistical) @xmath4, and this ethical concern should be further investigated. in this section, we explore the following ethical issues. target user: according to the report of statistics, a typical active trial judge closed around 250 cases in a year. trial judges suffering from 'daunting workload' is becoming an critical issue @xcite . the proposed algorithm is designed for generating the court's view draft for assisting the trial judges for decision making. this work is an algorithmic investigation, but such algorithm should never 'replace' human judges. human knowledge/judgment should be the final safeguard to protect social justice and individual fairness. ## conclusion and future work in this paper, we propose a novel attentional and counterfactual based natural language genera-tion (ac-nlg) method to solve the task of court's view generation in civil cases and ensure the fairness of the judgment. we design a claim-aware encoder to represent the fact description which emphasizes on the plaintiff's claim, as well as a pair of backdoor-inspired counterfactual decoders to generate judgment-discriminative court's views (both supportive and non-supportive views) and to eliminate the bias that arose from the data generation mechanism by connecting with a synergistic judgment predictive model. the experimental results show the effectiveness of our method. based on the ac-nlg method, in the future, we can explore the following directions: (1) improve the accuracy of judgment on a claim-level. (2) add external knowledge (e.g. a logic graph) to the predictor for the interpretability of the model.