Correlation Metrics for Safe Artificial Intelligence

Golnoosh Babaei and Paolo Giudici ()
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Golnoosh Babaei: Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Paolo Giudici: Department of Economics and Management, University of Pavia, 27100 Pavia, Italy

Risks, 2025, vol. 13, issue 9, 1-12

Abstract: There is a growing need to provide AI risk management models that can assess whether AI applications are safe and trustworthy, to make them responsible. To date, there are a few research papers on this topic. To fill the gap, in this paper we extend the recently proposed SAFE framework, a comprehensive approach to measure AI risks across four key dimensions: security, accuracy, fairness, and explainability (SAFE). We contribute to the SAFE framework with a novel use of the coefficient of determination ( R 2 ) to quantify deviations from ideal behavior not only in terms of accuracy but also for security, fairness, and explainability. Our empirical findings shows the effectiveness of the proposal, which leads to a more precise measurement of risks of AI regression applications, which involve the prediction of continuous response variables.

Keywords: SAFE AI metrics; responsible AI; coefficient of determination (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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