A Trust-Centric Framework for Secure, User-Centric AI Systems with Actionable and Personalized Recourse

Abstract

Artificial Intelligence (AI) applications increasingly mediate high-stakes decisions in domains such as finance, healthcare, and social welfare. Yet their deployment often exposes users and institutions to risks arising from bias, opacity, and technical vulnerabilities. High predictive accuracy alone fails to ensure trust, particularly in contexts where user engagement, recourse, and accountability are critical. These challenges motivate a principled, layered approach to operationalizing trust in AI. This dissertation presents a trust-centric AI framework that formalizes trust as a multi-layer architectural objective. Trust is structured across three interdependent layers: technical trust, ensuring model integrity, security, and resilience against adversarial attacks; cognitive and practical trust, realized through causally grounded, feasible, cost-aware, and interpretable counterfactual explanations; and user-aligned trust, achieved by leveraging preference-aware, lexicographic optimization that personalizes actionable recourse. By aligning these layers through secure deployment mechanisms, causal verification, and multi-objective optimization, the framework shows how failures in security, explainability, or personalization can be systematically mitigated within a coherent trust-centric design. The framework is evaluated within tabular and structured decision-making environments, providing a testbed for secure model deployment, causally grounded explanations, and preference-aware actionable recourse. These contexts illustrate how layered trust can reconcile predictive accuracy, interpretability, and user agency under uncertainty and high personal stakes. By embedding verifiable, actionable, and user-aligned trust into AI-driven systems, this work supports responsible deployment in high-stakes domains and provides technical guidance for policymakers, organizations, and system architects seeking robust, transparent, and user-centered AI.