8 citations · 21 across the 34 of their papers we have counts for
34 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…
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…
ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Yuqicheng Zhu, Nico Potyka, Daniel Hernández +6
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sen…
Argumentative Debates for Transparent Bias Detection [Technical Report]
Hamed Ayoobi, Nico Potyka, Anna Rapberger +1
As the use of AI in society grows, addressing emerging biases is essential to prevent systematic discrimination. Several bias detection methods have been proposed, but, with few ex…
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…
Privacy-Preserving Inconsistency Measurement
Carl Corea, Timotheus Kampik, Nico Potyka
We investigate a new form of (privacy-preserving) inconsistency measurement for multi-party communication. Intuitively, for two knowledge bases K_A, K_B (of two agents A, B), our r…
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…
Conformalized Answer Set Prediction for Knowledge Graph Embedding
Yuqicheng Zhu, Nico Potyka, Jiarong Pan +4
Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The l…
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction
Yuqicheng Zhu, Nico Potyka, Mojtaba Nayyeri +4
Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link…
Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
Yuqicheng Zhu, Nico Potyka, Bo Xiong +4
Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilisti…
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…
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…
Promoting Counterfactual Robustness through Diversity
Francesco Leofante, Nico Potyka
Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e.g., when a l…
Robust Knowledge Extraction from Large Language Models using Social Choice Theory
Nico Potyka, Yuqicheng Zhu, Yunjie He +2
Large-language models (LLMs) can support a wide range of applications like conversational agents, creative writing or general query answering. However, they are ill-suited for quer…
ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]
Hamed Ayoobi, Nico Potyka, Francesca Toni
We propose ProtoArgNet, a novel interpretable deep neural architecture for image classification in the spirit of prototypical-part-learning as found, e.g., in ProtoPNet. While earl…
Understanding ProbLog as Probabilistic Argumentation
Francesca Toni, Nico Potyka, Markus Ulbricht +1
ProbLog is a popular probabilistic logic programming language/tool, widely used for applications requiring to deal with inherent uncertainties in structured domains. In this paper…
Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)
Xiang Yin, Nico Potyka, Francesca Toni
Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). Whil…
Non-flat ABA is an Instance of Bipolar Argumentation
Markus Ulbricht, Nico Potyka, Anna Rapberger +1
Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism, whereby arguments and attacks between them are drawn from rules, defeasible assumptions and…
SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report]
Hamed Ayoobi, Nico Potyka, Francesca Toni
Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging. Existing approaches often focus on explaining how changing individual inp…
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,…
Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data
Nico Potyka, Xiang Yin, Francesca Toni
There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term. We revisit fait…
Faithiful Embeddings for EL++ Knowledge Bases
Bo Xiong, Nico Potyka, Trung-Kien Tran +2
Recently, increasing efforts are put into learning continual representations for symbolic knowledge bases (KBs). However, these approaches either only embed the data-level knowledg…