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This position paper advocates computational argumentation as a formal foundation for Evaluative AI, which supports human decision-making by presenting competing hypotheses with evidence for and against, rather than single recommendations.
Introduces CANOE, a multi-agent neuro-symbolic framework for open-ended care plan coordination that uses argumentative computation and human-in-the-loop contestation to improve transparency, safety, and clinical correctness.
This paper presents a 17k-sentence corpus with annotations for argumentative passages across three German political arenas during COVID-19, and a pilot study on automatically identifying such passages, finding that boundaries are hard to pin down and models exhibit confirmation bias.
This paper proposes a framework that uses the Toulmin model of argumentation to structure ML-based retinal diagnosis from OCT images, integrating biomarker extraction, medical LLM reasoning (MedGemma), and similarity measures (MedSigLip) for interpretable and evidence-based diagnostic assistance.
This paper introduces a categorical framework for constructing verifiable, local truth-preserving foundation models using composable foundries, implemented in the Odyssey system, and scheduled for a tutorial at ICML 2026.