19 citations · 72 across the 35 of their papers we have counts for
10 papers · 1 filter
Argumentation and Machine Learning
Antonio Rago, Kristijonas Čyras, Jack Mumford +1
This chapter provides an overview of research works that present approaches with some degree of cross-fertilisation between Computational Argumentation and Machine Learning. Our re…
A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
Antonio Rago, Stylianos Loukas Vasileiou, Francesca Toni +2
Gradual semantics (GS) have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-wor…
Exploring the Effect of Explanation Content and Format on User Comprehension and Trust in Healthcare
Antonio Rago, Bence Palfi, Purin Sukpanichnant +5
AI-driven tools for healthcare are widely acknowledged as potentially beneficial to health practitioners and patients, e.g. the QCancer regression tool for cancer risk prediction.…
Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Xuehao Zhai, Junqi Jiang, Adam Dejl +4
Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitate…
Contestable AI needs Computational Argumentation
Francesco Leofante, Hamed Ayoobi, Adam Dejl +10
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…
Argumentative Large Language Models for Explainable and Contestable Claim Verification
Gabriel Freedman, Adam Dejl, Deniz Gorur +3
The profusion of knowledge encoded in large language models (LLMs) and their ability to apply this knowledge zero-shot in a range of settings makes them promising candidates for us…
Interval Abstractions for Robust Counterfactual Explanations
Junqi Jiang, Francesco Leofante, Antonio Rago +1
Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI research, providing recourse recommendations for users affected by the decisions of machine lea…
Can Large Language Models perform Relation-based Argument Mining?
Deniz Gorur, Antonio Rago, Francesca Toni
Argument mining (AM) is the process of automatically extracting arguments, their components and/or relations amongst arguments and components from text. As the number of platforms…
Robust Counterfactual Explanations in Machine Learning: A Survey
Junqi Jiang, Francesco Leofante, Antonio Rago +1
Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While…
Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)
Junqi Jiang, Antonio Rago, Francesco Leofante +1
Model Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task. Recent studies show that models obtained…