A Methodology for Non-Functional Property Evaluation of Machine Learning Models

The pervasive diffusion of Machine Learning (ML) in many critical domains and application scenarios has revolutionized implementation and working of modern IT systems. The behavior of modern systems often depends on the behavior of ML models, which are treated as black boxes, thus making automated decisions based on inference unpredictable. In this context, there is an increasing need of verifying the non-functional properties of ML models, such as, fairness and privacy, to the aim of providing certified ML-based applications and services. In this paper, we propose a methodology based on Multi-Armed Bandit for evaluating non-functional properties of ML models. Our methodology adopts Thompson sampling, Monte Carlo Simulation, and Value Remaining. An experimental evaluation in a real-world scenario is presented to prove the applicability of our approach in evaluating the fairness of different ML models.