Case-based reasoning (CBR) is central to legal practice, relying on precedents to interpret and apply the law. Various formalisms have been proposed to represent cases, including cases represented as situation-outcome pairs (situation-outcome cases) and cases represented as logical formulas (logical cases). Connections between situation-outcome cases and logical cases have been preliminary explored, but interoperability between CBR models of these representations remains underexamined. To address this gap, this paper introduces four formal tools: (1) compatibility, which concerns interpolating a logical case into multiple situation-outcome cases; (2) enumerators, which generalise compatibility by interpolating logical case models into valid situation-outcome cases; (3) prototypers, which relate logical case models to reflexive and consistent situation-outcome CBR models, as illustrated by AA-CBR and the result model of precedential constraint; and (4) translators, which attempting the reverse, namely connecting such situation-outcome CBR models back to logical case models. Our investigation of these tools reveal how implicit cases can be introduced to simulate or align with another CBR model’s reasoning, which contributes to interoperability between CBR models.

In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument. We then demonstrate how this can be applied to a real-world medical dataset sourced from a clinical trial evaluating differing assessment methods of patients with a primary brain tumour. We show empirically that our approach outperforms other interpretable machine learning models on this dataset.

AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can 1. interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and 2. revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support.

Case-based reasoning is known to play an important role in several legal settings. We focus on a recent approach to case-based reasoning, supported by an instantiation of abstract argumentation whereby arguments represent cases and attack between arguments results from outcome disagreement between cases and a notion of relevance. We explore how relevance can be learnt automatically with the help of decision trees, and explore the combination of case-based reasoning with abstract argumentation (AA-CBR) and learning of case relevance for prediction in legal settings. Specifically, we show that, for two legal datasets, AA-CBR with decision-tree-based learning of case relevance performs competitively in comparison with decision trees, and that AA-CBR with decision-tree-based learning of case relevance results in a more compact representation than their decision tree counterparts, which could facilitate cognitively tractable explanations.

Recently, abstract argumentation-based models of case-based reasoning (AA-CBR in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios. However, the formal properties of AA-CBR as a reasoning system remain largely unexplored. In this paper, we focus on analysing the non-monotonicity properties of a regular version of AA-CBR (that we call AA-CBR_>). Specifically, we prove that AA-CBR_> is not cautiously monotonic, a property frequently considered desirable in the literature. We then define a variation of AA-CBR_> which is cautiously monotonic. Further, we prove that such variation is equivalent to using AA-CBR_> with a restricted casebase consisting of all "surprising" and "sufficient" cases in the original casebase. As a by-product, we prove that this variation of AA-CBR_> is cumulative, rationally monotonic, and empowers a principled treatment of noise in "incoherent" casebases. Finally, we illustrate AA-CBR and cautious monotonicity questions on a case study on the U.S. Trade Secrets domain, a legal casebase.

In order to practice a legal profession in Brazil, law graduates must be approved in the OAB national unified bar exam.For their topic coverage and national reach, the OAB exams provide an excellent benchmark for the performance of legal information systems, as it provides objective metrics and are challenging even for humans, as only 20% of its candidates are approved.After constructing a new data set on the exams and doing shallow experiments on it, we now employ the OpenWordnet-PT to verify whether using word senses and relations we can improve previous results.We discuss the results, possible future ideas and the additions to the OpenWordnet-PT that we made.