Synthetic Ethical Reasoning (SER): A Framework for Computational Ethics in Autonomous Systems

This research on Synthetic Ethical Reasoning (SER) establishes a comprehensive theoretical and computational foundation for creating artificial systems capable of sound ethical decision-making that is both transparent and contextually adaptive. Unlike fragmented approaches that isolate symbolic logic from machine learning, SER introduces a hybrid cognitive framework enabling a dynamic balance between rigid ethical principles and the flexibility required to navigate complex, unforeseen human situations. Grounded in deontic logic, decision theory, and reinforcement learning, SER integrates: An Ethical Knowledge Base (EKB):An explicit representational system encoding ethical principles (e.g., "do no harm," "maximize overall well-being") as first-order deontic logic formulas. This ensures that decisions are anchored to a clear, auditable ethical foundation. A Symbolic Reasoning Engine:A mechanism utilizing Answer Set Programming (ASP) to evaluate potential actions against the principles stored in the EKB, generating quantitative "compliance scores" that reflect the ethical implications of each possible choice. A Meta-Ethical Controller:A learning component (machine learning) that dynamically adjusts the weights of the ethical principles based on human feedback. This adaptive mechanism is what grants the system the ability to navigate cultural and contextual variations, learning the nuanced ethical preferences of different users or societies. Empirical simulations demonstrate that SER-enabled agents achieve up to 37% higher ethical alignment compared to static rule-based systems, while maintaining competitive task performance. Furthermore, its adaptive capability allows it to resolve complex moral dilemmas (such as trolley-problem variants) in a manner that more closely aligns with human judgments, effectively bridging the gap between artificial logic and human intuition. SER thus pioneers a new synthetic science of ethical reasoning—one that moves beyond rigid or "black box" models toward AI systems capable not only of moral reasoning, but also of justifying their decisions and adapting to the diverse ethical fabric of society.