Recent advances in machine learning and artificial intelligence are now being applied in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently unable to provide convincing arguments that systems using complex software implemented using machine learning algorithms are safe and correct.

In this paper, we present an efficient method to extract equivalence classes from decision trees and random forests, and to formally verify that their input/output mappings comply with requirements. We implement the method in our tool VoRF (Verifier of Random Forests), and evaluate its scalability on two case studies found in the literature. We demonstrate that our method is practical for random forests trained on low-dimensional data with up to 25 decision trees, each with a tree depth of 20. Our work also demonstrates the limitations of the method with high-dimensional data and touches upon the trade-off between large number of trees and time taken for verification.

Recent advances in machine learning and artificial intelligence are now beingconsidered in safety-critical autonomous systems where software defects maycause severe harm to humans and the environment. Design organizations in thesedomains are currently unable to provide convincing arguments that their systemsare safe to operate when machine learning algorithms are used to implement theirsoftware.

In this paper, we present an efficient method to extract equivalence classes from decision trees and tree ensembles, and to formally verify that their input-output mappings comply with requirements. The idea is that, given that safety requirements can be traced to desirable properties on system input-output patterns, we can use positive verification outcomes in safety arguments.

This paper presents the implementation of the method in the tool VoTE (Verifier of Tree Ensembles), and evaluates its scalability on two case studies presented in current literature. We demonstrate that our method is practical for tree ensembles trained on low-dimensional data with up to 25 decision trees and tree depths of up to 20.Our work also studies the limitations of the method with high-dimensionaldata and preliminarily investigates the trade-off between large number of trees and time taken for verification.

Recent advances in machine learning are now being considered for integration in safety-critical systems such as vehicles, medical equipment and critical infrastructure. However, organizations in these domains are currently unable to provide convincing arguments that systems integrating machine learning technologies are safe to operate in their intended environments.

In this paper, we present a formal verification method for tree ensembles that leverage an abstraction-refinement approach to counteract combinatorial explosion. We implemented the method as an extension to a tool named VoTE, and demonstrate its applicability by verifying the robustness against perturbations in random forests and gradient boosting machines in two case studies. Our abstraction-refinement based extension to VoTE improves the performance by several orders of magnitude, scaling to tree ensembles with up to 50 trees with depth 10, trained on high-dimensional data.