A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments. Existing approaches often lack the adaptability needed to integrate ethical principles into dynamic and ambiguous contexts, limiting their effectiveness across diverse scenarios. To address these challenges, we outline the necessary ingredients for building a holistic, meta-level framework that combines intermediate representations, probabilistic reasoning, and knowledge representation. The specifications therein emphasize scalability, supporting ethical reasoning at both individual decision-making levels and within the collective dynamics of multi-agent systems. By integrating theoretical principles with contextual factors, it facilitates structured and context-aware decision-making, ensuring alignment with overarching ethical standards. We further explore proposed theorems outlining how ethical reasoners should operate, offering a foundation for practical implementation. These constructs aim to support the development of robust and ethically reliable AI systems capable of navigating the complexities of real-world moral decision-making scenarios.

The deployment of autonomous agents that interact with humans in safety-critical situations raises new research problems as we move towards fully autonomous systems in domains such as autonomous vehicles or search and rescue. If autonomous agents are placed in a dilemma, how would they act? The literature in computational ethics has explored the actions and learning methods that emerge in ethical dilemmas. However, our position paper examines how ethical dilemmas are not isolated in a social vacuum. Our central claim in our position paper is that to enable trust among all human users, a neuralsymbolic verification of moral preference alignment is required. We propose that the formal robustness properties be applied to social choice modelling. We outline how robustness properties can help validate the formation of stable social preference clusters in deep neural network classifiers. Our initial results highlight the vulnerabilities of models in moral-critical scenarios to perturbations, suggesting a verification-training loop for improved robustness. We position this work as an inquiry into the viability of verifying moral preference alignment, based on our initial results. Ultimately, we aim to contribute to the broader interdisciplinary effort that integrates formal methods, social choice theory, and empirical moral psychology for interpretable computational ethics.