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Found 3 papers, 2 papers with code
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+2
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach
Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models.
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models.
