Explainable Artificial Intelligence requires appropriate statistical metrics to assess explainability and, more generally, trustworthiness of machine learning output. Examples of such metrics include measures of predictive accuracy (mean squared error, brier score, area under the curve, rank graduation accuracy) and related statistical tests (exact, asymptotic, bootstrapped); measures of explainability (difference in predictions, prediction ranks, explained variance, predictive accuracy).; regularisation methods to reduce computational complexity and improve interpretability (lasso and ridge, stepwise selection, dimensionality reduction, network analysis), and related model comparison tests; measures of robustness, under data perturbations, outliers and adversarial data (sensitivity analysis, bayesian robustness, extreme value models, bayesian robustness); measures of fairness ( group based measures, conditional measures, propensity score matching, counterfactual measures). The special track is focused on the above issues, but also their practical application in different application settings, such as automotive, finance, health and robotics. More generally, It aims at providing statistically sound solutions to the above problems, to enhance AI risk management, and make AI applications more responsible. The track is jointly organised by the two editors of the journal “Statistics” of Taylor and Francis. The Journal will organise, rightly after the conference, a special issue that 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.


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

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