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Found 6 papers, 3 papers with code
Studying Large Language Model Behaviors Under Realistic Knowledge Conflicts
In cases where the models still fail to update their answers, we find a parametric bias: the incorrect parametric answer appearing in context makes the knowledge update likelier to fail.
Feature Attribution Explanations for Spiking Neural Networks
We present \textit{Temporal Spike Attribution} (TSA), a local explanation method for SNNs.
Exploring Practitioner Perspectives On Training Data Attribution Explanations
Explainable AI (XAI) aims to provide insight into opaque model reasoning to humans and as such is an interdisciplinary field by nature.
Trustworthy Machine Learning
Collectively, we face a trustworthiness issue with the current machine learning technology.
Out-of-Distribution Generalization
Uncertainty Quantification
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
We recommend that future researchers and practitioners trust TDA estimates only in such cases.
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
