A Pragmatic Integrity Framework for Moral AI Alignment

Thesis Abstract

This study addresses the escalating challenge of aligning artificial intelligence systems with robust moral norms in complex, real-world environments where normative judgments are context-dependent and prone to ambiguity. The central aim is to develop a Pragmatic Integrity Framework (PIF) that operationalizes moral alignment across diverse domains by integrating virtue-theoretic considerations with practical constraint-satisfaction mechanisms. Specific objectives are (1) to conceptualize integrity as a situated, pluralistic virtue-based standard that governs AI behavior under uncertainty; (2) to identify operationalizable moral constraints drawn from Aristotle’s virtue ethics, deontological duties, and consequentialist considerations that can be encoded within AI decision pipelines; (3) to design a framework that mediates between abstract ethical prescriptions and implementable objective functions through a modular integrity layer; (4) to empirically validate the framework through multi-domain simulations and stakeholder-informed case studies; and (5) to provide evaluative metrics for pragmatic moral performance and robustness under distributional shifts. Methodologically, the research adopts a mixed-methods design combining normative analysis, computational modeling, and empirical validation. The population comprises AI systems deployed in three representative domains healthcare decision-support, autonomous transportation, and financial advisory services. A purposive sample of 12 AI systems (four per domain), each with differing architectures (rule-based, reinforcement learning, and hybrid models), will be analyzed. Data collection employs (a) expert interviews with 20 ethicists and domain practitioners to elicit contextual moral constraints; (b) a repository of 200 curated decision scenarios reflecting real-world ambiguity and potential harm; (c) simulation environments that implement controlled perturbations (sensor noise, adversarial inputs, and distributional shifts). Instruments include a coding scheme for integrity-laden constraints derived from virtue-ethics, deontic rules, and teleological considerations; a behavioral audit checklist to capture alignment outcomes; and a measurement instrument for pragmatic sufficiency, including constraint satisfaction scores and harm-avoidance indices. Validity and reliability are addressed via triangulation across normative theory, stakeholder perspectives, and empirical performance, with inter-rater reliability targets (Cohen’s kappa ? 0.70) for qualitative codings and internal consistency (Cronbach’s alpha ? 0.80) for quantitative scales. Data analysis proceeds in three layers. First, a normative-empirical synthesis analyzes the alignment constraints against observed AI actions to identify gaps between idealized principles and implementable behavior. Second, regression analyses quantify the relationship between integrity-layer adherence (independent variable) and outcome quality across domains, controlling for model complexity and data quality. Third, thematic analysis of interview data unpacks contextual factors shaping moral judgments, informing iterative refinements of the PIF. A model specification is developed to formalize the Pragmatic Integrity Framework as an optimization layer that interfaces with existing objective functions via a constrained multi-objective optimization approach, incorporating bounded rationality and virtue-based regularizers. Sensitivity analyses assess robustness to perturbations in constraints and distributional shifts, and scenario-based evaluations compare performance with baseline moral specifications. Expected findings indicate that the Pragmatic Integrity Framework yields measurable improvements in harm reduction, fairness indicators, and user trust, without sacrificing task performance, particularly under uncertain or adversarial conditions. The study anticipates identifying domain-specific integrity trade-offs and demonstrating that virtue-informed constraints can be encoded as adaptable priors or regularizers that steer optimization without prohibitive computational overhead. The contribution to knowledge lies in operationalizing a theoretically grounded, pragmatically actionable framework that harmonizes normative ethics with engineering feasibility, thereby advancing the design of robust, morally accountable AI systems. The main conclusion posits that moral alignment is best achieved through a dynamic integrity layer that translates pluralistic ethical theories into context-sensitive constraints, enabling scalable, domain-appropriate moral behavior. Recommendations include integrating the Pragmatic Integrity Framework into AI governance protocols, developing sector-specific constraint libraries, and fostering participatory design processes with diverse Stakeholder groups to continually refine integrity criteria as AI systems learn and evolve.