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Found 3 papers, 2 papers with code
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
This gives rise to ground-truth 'world explanations' for gender classification tasks, enabling the objective evaluation of the correctness of XAI methods.
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
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.
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)
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