From House of Quality to Neural Architecture: Quality-Informed Neural Networks for Interpretable Classification, with an EU AI Act Compliance Application
Andreea Cristina Ionica, Monica Leba · Systems · 2026
As software systems increasingly combine machine learning, deep learning, and generative AI components with classical deterministic logic, the systematic detection of AI-based algorithmic elements in application code is becoming essential for software audit, compliance with the EU AI Act (Regulation (EU) 2024/1689), and quality assurance. This paper introduces Quality-Informed Neural Networks (QINN), an architecture in which the structured knowledge encoded in the Quality Function Deployment (QFD) House of Quality is embedded into the network topology and weight initialisation through QFD-derived binary structural masks and knowledge-calibrated initialisation—in direct analogy with Physics-Informed Neural Networks (PINNs). The QFD relationship matrices act as structural priors that constrain the hypothesis space toward quality-consistent solutions by enforcing domain-expert-validated sparsity on network connectivity, while an optional QFD-regularised loss term provides an additional soft constraint on the learned weight structure. As a proof of concept, QINN is instantiated in its masked-architecture configuration for the binary classification of software repositories as AI-enabled or classical. On the AIC-199 proof-of-concept dataset, the proposed QINN attains a cross-validated AUC of 99.47% (±1.18%), recall of 100.00% (±0.00%), and F1-score of 99.02% (±1.34%) under QFD-informed structural masking, outperforming a non-learned QFD scoring baseline by 37.37 percentage points in recall and exceeding a cross-validated Random Forest ensemble on AUC by 2.47 percentage points (W = 0, p < 0.05), while producing explanations at three QFD-grounded levels—feature salience, named Technical-Evidence activations, and per-criterion quality requirement scores—that align directly with the EU AI Act documentation obligations. Validation on larger, independently curated datasets and sensitivity analysis of the QFD elicitation process are identified as priorities for future work. A domain-general seven-phase application protocol is provided.