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Found 4 papers, 3 papers with code
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance
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.
Benchmark data to study the influence of pre-training on explanation performance in MR image classification
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
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables
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'.
Scrutinizing XAI using linear ground-truth data with suppressor variables
It has been demonstrated that some saliency methods can highlight features that have no statistical association with the prediction target (suppressor variables).
Explainable Artificial Intelligence (XAI)
Feature Importance
