no code implementations • 20 May 2024 • Benedict Clark, Rick Wilming, Artur Dox, Paul Eschenbach, Sami Hached, Daniel Jin Wodke, Michias Taye Zewdie, Uladzislau Bruila, Marta Oliveira, Hjalmar Schulz, Luca Matteo Cornils, Danny Panknin, Ahcène Boubekki, Stefan Haufe
The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalisation and empirical validation, being an inherently unsupervised process.
1 code implementation • 22 Jun 2023 • Benedict Clark, Rick Wilming, Stefan Haufe
The field of 'explainable' artificial intelligence (XAI) has produced highly cited methods that seek to make the decisions of complex machine learning (ML) methods 'understandable' to humans, for example by attributing 'importance' scores to input features.
1 code implementation • 21 Jun 2023 • Marta Oliveira, Rick Wilming, Benedict Clark, Céline Budding, Fabian Eitel, Kerstin Ritter, Stefan Haufe
Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2
no code implementations • 2 Jun 2023 • Rick Wilming, Leo Kieslich, Benedict Clark, Stefan Haufe
In recent years, the community of 'explainable artificial intelligence' (XAI) has created a vast body of methods to bridge a perceived gap between model 'complexity' and 'interpretability'.