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Found 6 papers, 2 papers with code
Enabling Synthetic Data adoption in regulated domains
From a prescriptive viewpoint, eventually, DAISYnt may pave the way to synthetic data adoption in highly regulated domains, ranging from Finance to Healthcare, through Insurance and Education.
Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes
Recently, also machine and deep learning techniques have been applied to the CRM task, showing an important increase in prediction quality and performances.
PSD2 Explainable AI Model for Credit Scoring
SHAP package is used to provide a global and local interpretation of the model predictions to formulate a human-comprehensive approach to understanding the decision-maker algorithm.
Metrics for Multi-Class Classification: an Overview
Performance indicators are very useful when the aim is to evaluate and compare different classification models or machine learning techniques.
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms
In this paper, we highlight a trade-off between explanation's stability and adherence, namely how much it resembles the ML model.
Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models
We test LIME on the Machine Learning algorithm and check its stability.
