Argumentation for Explainable and Globally Contestable Decision Support with LLMs

TL;DR

This paper introduces ArgEval, a framework that enhances LLM-based decision support by enabling global contestability through structured evaluation of decision options and argumentation frameworks.

Contribution

ArgEval shifts from local, instance-specific reasoning to structured, global evaluation of decision options, allowing for more transparent and contestable decision support.

Findings

ArgEval effectively supports treatment recommendation for glioblastoma.

It produces explainable guidance aligned with clinical practice.

Framework enables global contestability by modifying shared argumentation frameworks.

Abstract

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.