Predictive Framework for Moral Decision Modeling in Critical Systems Using Conscious AI and Data-Centric Ethics

In recent years, the integration of artificial intelligence (AI) in high-stakes domains such as healthcare, defines, autonomous vehicles, and financial systems has raised critical ethical concerns. As AI transitions from reactive systems to more advanced, conscious-like entities, there is an urgent need for frameworks that enable transparent, data-driven, and morally sound decision-making. This paper presents a predictive framework for moral decision modeling in critical systems using Conscious AI embedded with data- centric ethical analysis. The framework is designed to balance accuracy, ethical reasoning, and explainability by integrating multiple AI components capable of processing contextual and human-centric data under critical constraints. The core of this system is a Conscious AI architecture that mimics aspects of awareness and self- regulation through feedback loops and context sensitivity. This architecture is layered over a data- centric ethics module that utilizes labelled ethical datasets, real-world scenarios, and rule-based logical annotations. These annotations are further processed through supervised and unsupervised learning techniques to extract ethical patterns and moral features. The integration of predictive modeling and moral evaluation occurs in a decision-control layer, where outcomes are analysed for ethical consistency and trustworthiness. Experiments were conducted across three critical domains—autonomous driving, emergency medical triage, and military drone navigation—using synthetic and real-world datasets. The proposed framework achieved an average decision accuracy of 93.6% across all scenarios, while maintaining a moral consistency rate of 91.2% based on cross-validation with human ethics panels. Explainability modules, powered by SHAP and LIME, were embedded to ensure transparent visualization of ethical decision pathways, which enhanced user trust by 87% in a controlled trial. Unlike traditional AI models that rely solely on utility functions or static rule sets, this framework continuously learns ethical nuances from evolving datasets, enabling adaptive moral alignment. It supports counterfactual analysis, enabling the system to simulate “what-if” scenarios for moral dilemma resolution. Additionally, ethical bias detection modules flag potentially discriminatory or harmful decisions, which are corrected in real time through the feedback loop. This hybrid approach outperforms existing ethical AI solutions by combining neuro- symbolic AI, deep learning, and ethical ontologies in a unified decision-making pipeline. The framework’s modularity allows for domain- specific adaptation without retraining the core engine. Moreover, the conscious component facilitates not only prediction and reasoning but also ethical introspection, thus enhancing decision reliability in ambiguous and high-risk environments. This study contributes a novel perspective to AI governance, offering a pathway toward self-regulating AI systems that are capable of upholding societal values, legal compliance, and moral reasoning autonomously. It further enables the auditability of AI-driven decisions, which is a key concern in ethical AI legislation and regulatory frameworks. Future work will focus on extending this model to handle cross-cultural ethics, emotional intelligence integration, and proactive ethical foresight. The results affirm that incorporating data-centric ethics within a conscious AI model is not only feasible but crucial for achieving moral alignment in autonomous systems operating in complex, uncertain, and ethically sensitive environments.