Trustworthy Artificial Intelligence Models for Regulated Decision Ecosystems: A Systematic and Empirical Framework
DOI:
https://doi.org/10.63856/ijis/v2i5/00041Keywords:
Trustworthy Artificial Intelligence; Regulated Decision Ecosystems; Explainable AI (XAI); Algorithmic Fairness; AI Governance; Bias Mitigation; Model Interpretability; Robust Machine Learning; Ethical AI Systems; Regulatory Compliance.Abstract
The proliferation of artificial intelligence (AI) in regulated domains — including consumer credit, clinical diagnosis, and public administration — has intensified regulatory and societal scrutiny of algorithmic decision-making. While predictive performance has improved substantially, persistent deficiencies in fairness, explainability, and accountability impede institutional adoption and regulatory compliance. This paper proposes and empirically evaluates the Regulated Trustworthy AI Lifecycle (RTAIL), a modular framework that integrates fairness-aware learning, post hoc interpretability, and robustness validation into a structured governance lifecycle. Using a synthetic loan approval dataset (N = 10,000; 18 features; sensitive attributes: gender, age group), we compare a logistic regression baseline against an RTAIL-compliant model employing Kamiran and Calders (2012) instance reweighting and SHAP-based explanation consistency. Across five-fold stratified cross-validation, the RTAIL model reduces the Demographic Parity Difference from 0.24 ± 0.03 to 0.08 ± 0.01 (66.7% reduction) and the Equal Opportunity Difference from 0.19 ± 0.02 to 0.06 ± 0.01 (68.4% reduction), at a statistically non-significant accuracy cost of 3.2 percentage points (McNemar p = 0.09). SHAP feature-importance consistency improves from 0.54 to 0.91. Ablation analysis confirms that the fairness constraint is the dominant driver of bias reduction. These results demonstrate that trustworthiness is an achievable, measurable property of AI system design — not merely a normative aspiration — and that the accuracy-fairness trade-off is navigable within operationally acceptable bounds for high-stakes regulated applications.
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