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Found 4 papers, 3 papers with code
Do not explain without context: addressing the blind spot of model explanations
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising greater interpretability.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Fooling Partial Dependence via Data Poisoning
We believe this to be the first work using a genetic algorithm for manipulating explanations, which is transferable as it generalizes both ways: in a model-agnostic and an explanation-agnostic manner.
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
The increasing amount of available data, computing power, and the constant pursuit for higher performance results in the growing complexity of predictive models.
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets
Data collected in this way is used to study the factors influencing the algorithm's performance.
