A Framework for Trustworthy AI Model Debugging and Validation

Thesis Abstract

Increasing deployment of complex AI systems across critical domains has amplified concerns about model reliability, transparency, and accountability, necessitating rigorous mechanisms for debugging and validation to ensure trustworthy performance under real-world conditions. This study addresses the gap between traditional software testing and AI-specific reliability practices by proposing a comprehensive framework for systematic debugging and multi-faceted validation that integrates technical, ethical, and governance dimensions. The aim is to operationalize trustworthy AI through a structured methodology that enables rapid detection, diagnosis, and remediation of failures while preserving fairness, robustness, and explainability. Specific objectives include (1) delineating a layered debugging protocol that identifies data, model, and deployment-level faults; (2) integrating a validation pipeline that combines quantitative metrics, qualitative explanations, and scenario-based stress testing; (3) developing an open, extensible toolchain that automates traceability, reproducibility, and documentation of corrective actions; (4) evaluating the framework across heterogeneous AI models and datasets to demonstrate generalizability; and (5) formulating governance guidelines that align debugging and validation practices with ethical and regulatory expectations. The study adopts a mixed-methods design, combining quantitative experimentation with qualitative expert assessments. The population comprises AI models deployed in healthcare, finance, and public-sector use cases, with a purposive sample of 12 models representing supervised, unsupervised, and reinforcement-learning paradigms. From these, 6 models will be selected for in-depth experimentation, with 3 additional industrial deployments for external validation. Data collection instruments include (i) a structured debugging checklist and logging schema capturing data provenance, model weights, and inference traces; (ii) a validation suite consisting of fairness metrics (equalized odds, demographic parity), robustness tests (adversarial perturbations, distribution shift simulations), calibration error measurements, and explanations (SHAP, LIME); (iii) scenario-based stress tests reflecting domain-specific risk events; and (iv) semi-structured interview guides for practitioners to assess usability, governance alignment, and perceived trust. Validity and reliability will be ensured through pilot testing (n=2 models), inter-rater reliability checks for qualitative coding (Cohen’s kappa ? 0.8), and instrument triangulation. Analytical methods include (i) defect taxonomy analysis to categorize debugging findings; (ii) descriptive statistics and inferential analyses (paired t-tests and repeated-measures ANOVA) to evaluate improvements in reliability, fairness, and robustness after debugging interventions; (iii) regression analysis to identify predictors of post-debug validation scores; (iv) Bayesian model checking to assess uncertainty and calibration; (v) qualitative thematic analysis of practitioner interviews to extract usability and governance insights; and (vi) a multi-criteria decision analysis (MCDA) framework to prioritize remediation actions. A reconciliation mechanism will map observed failures to the framework’s debugging stages, ensuring traceability and reproducibility. The theoretical underpinning integrates Safe AI principles with the principles of Responsible AI and a contextualized Debug-Validate–Govern framework, drawing on theories of software reliability, algorithmic fairness, and explainable AI (XAI). Expected findings include (i) a validated, modular framework that reduces time-to-detection by 40–50% and increases post-debug validation pass rates by at least 25%; (ii) evidence that combined quantitative-qualitative validation improves stakeholder trust and governance readiness; (iii) a taxonomy linking data drift and model fragility to specific remediation strategies; and (iv) actionable guidelines for integrating debugging and validation processes into standard AI life-cycle practices. The study contributes to knowledge by bridging the gap between traditional software debugging and AI-specific reliability assurance, offering a transferable, end-to-end framework that encompasses technical mechanisms, evaluation protocols, and governance considerations for trustworthy AI deployment. It will produce a publicly available toolchain prototype, a comprehensive validation dataset with labeled failure modes, and an implementable governance handbook for organizations. The main conclusion anticipates that a structured Debug-Validate-Govern framework materially enhances trust in AI systems without compromising innovation, and recommendations emphasize standardization of debugging logs, routine third-party audits, and ongoing stakeholder engagement to sustain trustworthy AI practices.