1 citations · 1 across the 2 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
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…