A statistical package for safe artificial intelligence A statistical package for safe artificial intelligence

• Published in: Statistical methods & applications Vol. 34; no. 3; pp. 499 - 517
• Main Authors: Babaei, Golnoosh, Giudici, Paolo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2025; Springer Nature B.V
• Subjects: Artificial intelligence; Chemistry and Earth Sciences; Computer Science; Economics; Finance; Health Sciences; Humanities; Insurance; Law; Machine learning; Management; Mathematics and Statistics; Medicine; Original Paper; Physics; Python; Reproducibility; Statistical methods; Statistical Theory and Methods; Statistics; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Sustainability; Lorenz curves; Python coding; Artificial intelligence; Machine learning
• ISSN: 1618-2510; 1613-981X
• DOI: 10.1007/s10260-025-00796-y

The rapid expansion of Artificial Intelligence (AI) applications necessitates the introduction of statistical methods and metrics that can assess their quality, not only from a technical viewpoint (accuracy, sustainability); but also from an ethical viewpoint (explainability, fairness). In this paper, we contribute to fill the gap proposing a set of consistent statistical metrics to measure the Sustainability, Accuracy, Fairness and Explainability of AI applications, integrated in an open-source Python package, which allows their full reproducibility. They are easy to interpret, as are all expressed in percentages of an ideal situation of full compliance. They are agnostic, as they can be applied to any Machine Learning method. They are fully reproducible, by means of the proposed Python safeaipackage, which serves as a convenient development environment for Python programmers.