A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
G Schwalbe, B Finzel - Data Mining and Knowledge Discovery, 2024 - Springer
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …
criteria have been developed within the research field of explainable artificial intelligence …
The next generation of medical decision support: A roadmap toward transparent expert companions
Increasing quality and performance of artificial intelligence (AI) in general and machine
learning (ML) in particular is followed by a wider use of these approaches in everyday life …
learning (ML) in particular is followed by a wider use of these approaches in everyday life …
[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …
outperforming human performance at certain tasks. There is no doubt that AI is important to …
[图书][B] Handbuch der künstlichen Intelligenz
G Görz, CR Rollinger, J Schneeberger - 2003 - degruyter.com
Liste der Autoren Page 1 Liste der Autoren Clemens Beckstein Gerhard Brewka Christian
Borgelt Wolfram Burgard Hans-Dieter Burkhard Stephan Busemann Thomas Christaller Leonie …
Borgelt Wolfram Burgard Hans-Dieter Burkhard Stephan Busemann Thomas Christaller Leonie …
[HTML][HTML] ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices
In machine learning, the presumably best model is selected from a variety of model
candidates generated by testing different model types, hyperparameters, or feature subsets …
candidates generated by testing different model types, hyperparameters, or feature subsets …
Xai handbook: towards a unified framework for explainable AI
The field of explainable AI (XAI) has quickly become a thriving and prolific community.
However, a silent, recurrent and acknowledged issue in this area is the lack of consensus …
However, a silent, recurrent and acknowledged issue in this area is the lack of consensus …
Generating explanations for conceptual validation of graph neural networks: An investigation of symbolic predicates learned on relevance-ranked sub-graphs
B Finzel, A Saranti, A Angerschmid, D Tafler… - KI-Künstliche …, 2022 - Springer
Abstract Graph Neural Networks (GNN) show good performance in relational data
classification. However, their contribution to concept learning and the validation of their …
classification. However, their contribution to concept learning and the validation of their …
Beneficial and harmful explanatory machine learning
Given the recent successes of Deep Learning in AI there has been increased interest in the
role and need for explanations in machine learned theories. A distinct notion in this context …
role and need for explanations in machine learned theories. A distinct notion in this context …
The grammar of interactive explanatory model analysis
The growing need for in-depth analysis of predictive models leads to a series of new
methods for explaining their local and global properties. Which of these methods is the best …
methods for explaining their local and global properties. Which of these methods is the best …
What is Missing in XAI So Far? An Interdisciplinary Perspective
With the perspective on applications of AI-technology, especially data intensive deep
learning approaches, the need for methods to control and understand such models has …
learning approaches, the need for methods to control and understand such models has …