## 5 Discussion
For example, Equal Opportunity Difference will be used to assess disparities in true positive rates between different user groups, while SHAP-based attribution analysis will be extended to detect systematicdifferences in feature importance across organizational units or complaint categories. These metrics will provide a more granular view of fairness and complement the current demographic audits. Although some sensitive attributes were pseudonymized, the framework is designed to support subgroup-level auditing where ethical and legal constraints permit access to such data. Nevertheless, ethical considerations were operationalized throughout the design and deployment of the system. The use of historical user interaction data followed strict data governance protocols, including pseudonymization of records, minimization of retained attributes, and internal access restrictions, in accordance with GDPR-compliant procedures. Although no direct personal identifiers were used in model training, certain optional fields such as email and phone numbers were included in dashboard views for administrative traceability. To mitigate re-identification risks, these fields were pseudonymized using irreversible hashing algorithms, and access to dashboards was limited to authorized personnel under role-based controls and audit logging. All modeling phases were subject to ex ante review by domain stakeholders to ensure alignment with administrative policies. The dataset was examined for representation bias across organizational units and equipment categories. Class imbalance was addressed using automatic class weighting during model training, and per-class performance metrics--such as F1-score and MCC--were used to detect disparities in predictive accuracy. Additionally, SHAP values were computed to explain classification outputs, highlighting the contribution of both structured features and textual terms. These explanations were presented to IT support staff to verify the interpretability and operational plausibility of the model's decisions. This combination of fairness-aware preprocessing, performance disaggregation, and post hoc explainability constitutes a practical framework for responsible AI deployment in public sector complaint management. The study also encountered certain limitations. The framework was tested within a single government entity, which may limit the generalizability of results to other contexts with different operational workflows or complaint structures. Additionally, the static nature of training data introduces the risk of model drift over time, particularly as user behavior, system updates, or policy changes alter the underlying patterns. Real-time adaptation remains a technical and organizational challenge, requiring continuous monitoring and retraining strategies that are not yet fully operationalized in the current implementation. Another important limitation relates to the semantic clustering approach itself. While BERT-based embeddings significantly improve complaint grouping compared to keyword methods, they are not immune to semantic drift--especially in the presence of rare technical jargon, domain-specific expressions, or evolving terminologies. Mislusterings can occur when the model's pretraining context differs from the operational vocabulary of a specific public agency. This suggests that domain-specific fine-tuning or adaptive retraining strategies may be necessary to maintain clustering accuracy over time, particularly as new complaint types or system updates emerge. When compared to traditional manual filtering approaches, the AI-driven system proved significantly more scalable, consistent, and responsive. Manual methods often rely on rigid keyword lists and human interpretation, leading to inconsistencies in classification and slower turnaround times. In contrast, the automated approach provided dynamic, adaptive classification capabilities, reduced labor requirements, and introduced a level of insight that manual methods are unlikely to achieve at scale.