Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
This addresses the need for ethical and transparent uncertainty explanation in LLMs for high-stakes domains, though it is incremental as it builds on existing moral psychology and rule-based approaches.
The paper tackles the problem of explaining uncertainty in large language models (LLMs) for high-stakes settings by proposing a framework based on rule-based moral principles, resulting in a transparent, lightweight alternative to probabilistic methods that improves trust and interpretability in domains like clinical and legal applications.
Large language models (LLMs) are increasingly used in high-stakes settings, where explaining uncertainty is both technical and ethical. Probabilistic methods are often opaque and misaligned with expectations of transparency. We propose a framework based on rule-based moral principles for handling uncertainty in LLM-generated text. Using insights from moral psychology and virtue ethics, we define rules such as precaution, deference, and responsibility to guide responses under epistemic or aleatoric uncertainty. These rules are encoded in a lightweight Prolog engine, where uncertainty levels (low, medium, high) trigger aligned system actions with plain-language rationales. Scenario-based simulations benchmark rule coverage, fairness, and trust calibration. Use cases in clinical and legal domains illustrate how moral reasoning can improve trust and interpretability. Our approach offers a transparent, lightweight alternative to probabilistic models for socially responsible natural language generation.