Statistics

Explainable Artificial Intelligence requires appropriate statistical metrics to assess explainability and, more generally, trustworthiness of machine learning output.

Following a special track jointly organised by both co-editors of Statistics, Paolo Giudici and Alexander Meister, at The 2nd World Conference on eXplainable Artificial Intelligence, the journal is excited to announce a special issue focused on Statistical Methods for Explainable Artificial Intelligence. This special issue can include the extended versions of the papers selected at the conference for the special track and, more generally, all selected papers that include statistical approaches for xAI. Topics for the special issue include, but are not limited to the following:

• Statistical tests for explainability
• Explainability as difference in predictions
• Explainability as difference in predictive accuracy
• Explainability as difference in goodness of fit
• Explainability as difference in concentration
• Model regularisation to improve explainability
• Lasso, Ridge and penalisation methods for improving xAI methods
• Principal components/dimension reduction methods for xAI methods improvement
• Improving robustness of explanations
• Influence functions for xAI methods
• Sensitivity analysis for xAI methods
• Outlier detection for xAI
• Reliability analysis in/for xAI methods
• Measure of fairness based on explainability
• Group based fairness for xAI
• Conditional fairness for/with xAI methods
• Propensity score matching for explainability
• Counterfactual fairness with/for xAI methods
• Statistical tests for fairness of xAI methods

When submitting your manuscript through the journal's Submission Portal, please confirm your submission is meant for a special issue when prompted. From there, select the appropriate special issue title from the dropdown menu that appears.

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