10 citations · 17 across the 13 of their papers we have counts for
13 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…
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…
Strength Change Explanations in Quantitative Argumentation
Timotheus Kampik, Xiang Yin, Nico Potyka +1
In order to make argumentation-based inference contestable, it is crucial to explain what changes can achieve a desired (instead of the contested) inference result. To this end, we…
Latent Debate: A Surrogate Framework for Interpreting LLM Thinking
Lihu Chen, Xiang Yin, Francesca Toni
Understanding the internal thinking process of Large Language Models (LLMs) and the cause of hallucinations remains a key challenge. To this end, we introduce latent debate, a nove…
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…
Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks
Xiang Yin, Nico Potyka, Francesca Toni
Explaining the strength of arguments under gradual semantics is receiving increasing attention. For example, various studies in the literature offer explanations by computing the a…
CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report)
Xiang Yin, Nico Potyka, Francesca Toni
There is a growing interest in understanding arguments' strength in Quantitative Bipolar Argumentation Frameworks (QBAFs). Most existing studies focus on attribution-based methods…
Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)
Xiang Yin, Potyka Nico, Francesca Toni
Quantitatively explaining the strength of arguments under gradual semantics has recently received increasing attention. Specifically, several works in the literature provide quanti…
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…
Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis
Timotheus Kampik, Nico Potyka, Xiang Yin +2
We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another. The introdu…
Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)
Nico Potyka, Xiang Yin, Francesca Toni
Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large,…