Trust Metrics in AI Models for Secure Governmental IT Systems

Abstract

Ensuring the trustworthiness of artificial intelligence (AI) models deployed within governmental IT infrastructures is an imperative mission-critical concern. As governments worldwide embrace AI-driven automation and decision support for applications ranging from citizen services to national security, they confront unique challenges: adversarial threats designed to deceive or corrupt models, the potential for biased outcomes that undermine fairness, opaque decision processes that erode stakeholder confidence, and the stringent regulatory and ethical obligations inherent in public-sector operations. This enhanced abstract delves into each dimension of trust—technical robustness, predictive accuracy, model explainability, fairness and non‑discrimination, data privacy and security, and governance oversight—highlighting how they interplay to form a holistic trust posture. We outline a composite Trust Score methodology that normalizes and weights individual sub‑metrics drawn from adversarial robustness testing, accuracy benchmarks, explainability indices (e.g., SHAP attributions), fairness audits (e.g., disparate impact ratios), privacy impact assessments, and compliance checklists mapped to governmental regulations. We discuss the methodological framework used to simulate real-world governmental IT deployments—including the generation of synthetic network telemetry, adversarial attack scenarios, data drift episodes, and policy-violation assessments—and present key findings: the Trust Score’s responsiveness to security breaches, its ability to flag fairness anomalies, and its sensitivity to governance lapses.