Correlation Metrics for Safe Artificial Intelligence

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 (R2) 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.