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Found 7 papers, 5 papers with code
Mining Action Rules for Defect Reduction Planning
In this paper, we introduce CounterACT, a Counterfactual ACTion rule mining approach that can generate defect reduction plans without black-box models.
Detection and Evaluation of bias-inducing Features in Machine learning
In this study, we propose an approach for systematically identifying all bias-inducing features of a model to help support the decision-making of domain experts.
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
The advantages of such models over classical ones are two-fold: 1) They grant users precise control over the level of transparency of the system and 2) They can potentially perform better than a standalone black box since redirecting some of the inputs to an interpretable model implicitly acts as regularization.
Understanding Interventional TreeSHAP : How and Why it Works
Shapley values are ubiquitous in interpretable Machine Learning due to their strong theoretical background and efficient implementation in the SHAP library.
Fool SHAP with Stealthily Biased Sampling
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution.
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
In this work, instead of aiming at reducing the under-specification of model explanations, we fully embrace it and extract logical statements about feature attributions that are consistent across all models with good empirical performance (i. e. all models in the Rashomon Set).
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Method: We conduct a Systematic Literature Review (SLR) of research papers published between 2015 to 2020, covering topics related to the certification of ML systems.
