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Found 4 papers, 0 papers with code
Example-based Explanations for Random Forests using Machine Unlearning
Despite their popularity and power, these models have been found to produce unexpected or discriminatory outcomes.
Counteracts: Testing Stereotypical Representation in Pre-trained Language Models
We evaluate 7 PLMs on 9 types of cloze-style prompt with different information and base knowledge.
Interpretable Data-Based Explanations for Fairness Debugging
We introduce Gopher, a system that produces compact, interpretable and causal explanations for bias or unexpected model behavior by identifying coherent subsets of the training data that are root-causes for this behavior.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals
There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to scrutinize and trust them.
