Large language models (LLMs) exhibit strong general capabilities, but their deployment in high-stakes domains is hindered by their opacity and unpredictability. Recent work has taken meaningful steps towards addressing these issues by augmenting LLMs with post-hoc reasoning based on computational argumentation, providing faithful explanations and enabling users to contest incorrect decisions. However, this paradigm is limited to pre-defined binary choices and only supports local contestation for specific instances, leaving the underlying decision logic unchanged and prone to repeated mistakes. In this paper, we introduce ArgEval, a framework that shifts from instance-specific reasoning to structured evaluation of general decision options. Rather than mining arguments solely for individual cases, ArgEval systematically maps task-specific decision spaces, builds corresponding option ontologies, and constructs general argumentation frameworks (AFs) for each option. These frameworks can then be instantiated to provide explainable recommendations for specific cases while still supporting global contestability through modification of the shared AFs. We investigate the effectiveness of ArgEval on treatment recommendation for glioblastoma, an aggressive brain tumour, and show that it can produce explainable guidance aligned with clinical practice.

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.

Evidence-based decision making is becoming increasingly important in many diverse domains, including healthcare, environmental management, and government. This has raised the need for tools to aggregate evidence from multiple sources. For instance, in healthcare, much valuable evidence is in the form of the results from clinical trials that compare the relative merits of treatments. For this, in a previous paper [5], we have proposed a general language for encoding, capturing and synthesizing knowledge from clinical trials and a framework that allows the construction and evaluation of arguments from such knowledge. Now, in this paper, we consider a specific version of the general framework for aggregating qualitative information about trials, and undertake an evaluation of this qualitative framework by comparing the results we obtain with those that are published in the biomedical literature. Whilst the results from our qualitative system are inferior, we show that they do offer a quick and useful aggregation of the evidence, and furthermore, we suggest that it could be coupled with information extraction technology to provide a valuable automated solution.

Evidence-based decision making is becoming increasingly important in healthcare. Much valuable evidence is in the form of the results from clinical trials that compare the relative merits of treatments. For this, in previous papers, we have proposed a general framework for representing and synthesizing knowledge from clinical trials involving the same outcome indicator. Now, in this paper, we present a new framework for representing and synthesizing knowledge from clinical trials involving multiple outcome indicators. In this framework, evidence from randomized clinical trials, systematic reviews, meta-analyses, network analyses, etc., comparing a pair of treatments τ1and τ2according to desired and/or undesired outcomes is aggregated to give an overall evaluation of the treatments saying τ1is superior to τ2, or τ1is equivalent to τ2, or τ1is inferior to τ2. Our general framework incorporates inference rules for generating arguments and counterarguments for claiming that one treatment is superior to another based on the available evidence, and preference rules for specifying which arguments are preferred. In this paper, we also present a new version of this framework that incorporates utility-theoretic criteria for defining specific preference rules over arguments.

We introduce a novel Ontology-based Argumentation Framework (OAF) that links a logic-based argumentation formalism and description logic ontologies. We show how these two formalisms can be tightly coupled by observing a few simple restrictions, and provides features not available in either formalism alone. Our work is evaluated in a large case study on decision-making in treatment choice in breast cancer, where rules are developed from the results of published clinical trials, and we present a small subset of this to demonstrate the use of the system. We show that OAF provides five advantages: (1) facilitating the clear use of shared definitions between multiple authors; (2) enabling us to match terms in the ontology and rules with those in the specific domain literature; (3) providing a close fit between structure of clinical trials and the structure of our rules; (4) delivering significant economies in the size of the rule-base compared to existing approaches; (5) allowing us take advantage of developments in both ontological and argumentative approaches. We also demonstrate that even a restricted language such as ours is sufficient to capture enough information to generate arguments that are useful for clinical practice. An early prototype implementation is available.