Аннотация
As Artificial Intelligence systems are increasingly deployed in safety-critical domains such as healthcare and finance, ensuring their trustworthiness and compliance is paramount. While Deep Neural Networks have received significant attention in formal verification, traditional models such as Decision Trees, often preferred for their interpretability, cannot inherently enforce constraints after training for fairness and stability. This paper presents a novel, comprehensive approach for the formal verification of Decision Tree classifiers using Satisfiability Modulo Theories (SMT). We propose a robust translation scheme that converts trained decision trees into logical constraints, enabling constraint inference that guarantees demographic parity and local robustness at prediction time. We implement this framework by using the z3 SMT solver and validate it on widely recognized fairness benchmarks, including UCI Adult, German Credit, and Loan Approval datasets. Experimental results demonstrate that our constrained model effectively eliminates demographic parity violations with a marginal accuracy trade-off of less than 0.2%. This approach transforms the SMT solver from a simple diagnostic tool into a provably fair inference engine suitable for regulated industries.