This survey highlights how optimization- and certification-oriented reasoning can provide a useful framework for reasoning about differences in models, supporting tasks ranging from model training and selection to auditing and certification.
Abstract
Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, including prediction, generation, and decision-making, models with similar empirical performance can exhibit markedly different properties in terms of their transparency, interpretability, robustness, fairness, privacy, and certifiability. This survey highlights how optimization- and certification-oriented reasoning can provide a useful framework for reasoning about such differences, supporting tasks ranging from model training and selection to auditing and certification. We review and synthesize recent advances at the intersection of combinatorial optimization (CO) and trustworthy ML, covering both training and post-training tasks, including interpretable model learning, explanation generation, robustness analysis, fairness auditing, model compression, and privacy attacks and protections. Across these domains, CO formulations offer additional capabilities over purely heuristic approaches, e.g., gradient-based ones, notably global guarantees, formal certificates, and explicit treatment of trade-offs. While scalability remains an important challenge, continued progress in solvers and hybrid algorithms suggests a growing role for CO in the design and deployment of trustworthy ML systems.
This paper introduces C LUE (Clustering and Experience-based Verification) , a training-free, non-parametric verifier that improves selection and reranking in Large Language Model outputs and finds that correct and incorrect solutions exhibit measurable geometric differences in their hidden-state trajectories.
Zhenwen Liang, Ruosen Li, Yujun Zhou et al.· Annual Meeting of the Associ...· 0 citations
This work compares three synthesis paradigms, statistical, adversarial, adversarial, and diffusion-based, on two benchmarks and provides task-driven guidance for selecting a synthesizer in learning analytics.
The minimal criterion under which a model may act as an oracle is derived, that trust must be purchased through selection-aware audits that are optimal in query complexity, and a dichotomy fixing when audited surrogates cut certified evaluation cost are proved.
This paper presents a structured and quantitative framework for evaluating the explainability of machine learning (ML) models trained on synthetic access control datasets. Explainability is assessed using four complementary metrics - stability, consistency, fidelity, and monotonicity - along with a normalized composite explainability score $E_{\text{norm}}$ that enables balanced comparison across models and datasets by addressing scale incompatibility among metrics. The framework is evaluated across four ML models (Logistic Regression, k-Nearest Neighbors, Naive Bayes, and a Neural Network) trained on six synthetic datasets ranging from rule-based and probabilistic to random and adversarial generation strategies. Results show that simpler models consistently produce more stable, consistent, and monotonic explanations on structured synthetic data, while neural networks exhibit greater variability and over-reliance on dominant features. Structured data generation methods were found to support stronger explainability than unstructured or adversarial approaches. Together, these findings provide practical guidance for selecting model-dataset combinations that support transparent and auditable decision-making in physical access control systems.
Ahmad N. Mohamad Jembari, Mustafa Abdallah· 2026 6th International Confe...· 0 citations
A systematic analysis of 207 studies selected from 4447 records following the PRISMA 2020 guidelines, covering work published between 2020 and 2026 across cybersecurity and computer vision finds systems that are robust against adaptive adversaries, interpretable under operational constraints, and auditable in environments where AI accountability is a legal requirement.
This paper proposes 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 and demonstrates that the constrained model effectively eliminates demographic parity violations.
A. Beishekeyev, T. Umarov· Herald of Kazakh-British tec...· 0 citations