49 citations · 80 across the 44 of their papers we have counts for
5 papers · 2 filters
llmSHAP: A Principled Approach to LLM Explainability
Filip Naudot, Tobias Sundqvist, Timotheus Kampik
Feature attribution methods help make machine learning-based inference explainable by determining how much one or several features have contributed to a model's output. A particula…
Change in Quantitative Bipolar Argumentation: Sufficient, Necessary, and Counterfactual Explanations
Timotheus Kampik, Kristijonas Čyras, José Ruiz Alarcón
This paper presents a formal approach to explaining change of inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and updating…
Set Contribution Functions for Quantitative Bipolar Argumentation and their Principles
Filip Naudot, Andreas Brännström, Vicenç Torra +1
We present functions that quantify the contribution of a set of arguments in quantitative bipolar argumentation graphs to (the final strength of) an argument of interest, a so-call…
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…
Conversational Process Model Redesign
Nataliia Klievtsova, Timotheus Kampik, Juergen Mangler +1
With the recent success of large language models (LLMs), the idea of AI-augmented Business Process Management systems is becoming more feasible. One of their essential characterist…