19 citations · 72 across the 35 of their papers we have counts for
37 papers
Contrastive Explanations in Quantitative Bipolar Argumentation Frameworks
Xiang Yin, Nico Potyka, Antonio Rago +1
Argumentation frameworks are useful tools for representing and reasoning with information in a variety of settings, e.g. in supplementing AI models as they perform classification t…
A Theory of Post-hoc Debate Judgement
Xiang Yin, Adam Dejl, Antonio Rago +2
Debates have recently emerged as a useful methodology for agentic AI to improve performance as well as to aid explainability and user engagement. For example, LLM-empowered agents…
Argumentation for Common Ground: Finding Zones of Possible Agreement between Individuals in Conflict
Elisa Cavatorta, Antonio Rago
How can common ground between societies in conflict be identified when citizens' acceptability of peace agreements is shaped by contested narratives? Such acceptability is mediated…
Towards an Argumentative Foundation for Evaluative AI
Xiang Yin, Tim Miller, Nico Potyka +2
Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together…
Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation
Eoghan Cunningham, Derek Greene, James Cross +1
Understanding how policy is debated and justified in parliament is a fundamental aspect of the democratic process. However, the volume and complexity of such debates mean that outs…
Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us
Stylianos Loukas Vasileiou, Antonio Rago, Francesca Toni +1
Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information and ext…
From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)
Aniol Civit, Antonio Rago, Antonio Andriella +2
Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It is considered a useful tool in d…
Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)
Deniz Gorur, Antonio Rago, Francesca Toni
Judgmental forecasting is the task of making predictions about future events based on human judgment. This task can be seen as a form of claim verification, where the claim corresp…
Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
Junqi Jiang, Francesco Leofante, Antonio Rago +1
Counterfactual explanations (CEs) provide recourse recommendations for individuals affected by algorithmic decisions. A key challenge is generating CEs that are robust against vari…
Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models
Kevin Zhou, Adam Dejl, Gabriel Freedman +3
Research in uncertainty quantification (UQ) for large language models (LLMs) is increasingly important towards guaranteeing the reliability of this groundbreaking technology. We ex…
Argumentatively Coherent Judgmental Forecasting
Deniz Gorur, Antonio Rago, Francesca Toni
Judgmental forecasting employs human opinions to make predictions about future events, rather than exclusively historical data as in quantitative forecasting. When these opinions f…
MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification
Ming Pok Ng, Junqi Jiang, Gabriel Freedman +2
Leveraging outputs from multiple large language models (LLMs) is emerging as a method for harnessing their power across a wide range of tasks while mitigating their capacity for ma…
On Gradual Semantics for Assumption-Based Argumentation
Anna Rapberger, Fabrizio Russo, Antonio Rago +1
In computational argumentation, gradual semantics are fine-grained alternatives to extension-based and labelling-based semantics . They ascribe a dialectical strength to (component…
Contestability in Quantitative Argumentation
Xiang Yin, Nico Potyka, Antonio Rago +2
Contestable AI requires that AI-driven decisions align with human preferences. While various forms of argumentation have been shown to support contestability, Edge-Weighted Quantit…
Argumentative Ensembling for Robust Recourse under Model Multiplicity
Junqi Jiang, Antonio Rago, Francesco Leofante +1
In machine learning, it is common to obtain multiple equally performing models for the same prediction task, e.g., when training neural networks with different random seeds. Model…
Representation Consistency for Accurate and Coherent LLM Answer Aggregation
Junqi Jiang, Tom Bewley, Salim I. Amoukou +4
Test-time scaling improves large language models' (LLMs) performance by allocating more compute budget during inference. To achieve this, existing methods often require intricate m…
Explainable Prediction of the Mechanical Properties of Composites with CNNs
Varun Raaghav, Dimitrios Bikos, Antonio Rago +2
Composites are amongst the most important materials manufactured today, as evidenced by their use in countless applications. In order to establish the suitability of composites in…
Free Argumentative Exchanges for Explaining Image Classifiers
Avinash Kori, Antonio Rago, Francesca Toni
Deep learning models are powerful image classifiers but their opacity hinders their trustworthiness. Explanation methods for capturing the reasoning process within these classifier…
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…