Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
Bibliographic record
Abstract
As large language models (LLMs) are increasingly used in high-stakes applications, the challenge of explaining uncertainty in natural language generation has become both a technical and moral imperative. Traditional approaches rely on probabilistic methods that are often opaque, difficult to interpret, and misaligned with human expectations of transparency and accountability. In response to these limitations, this paper introduces a novel framework based on rule-based moral principles-simple, human-inspired ethical guidelines-for responding to uncertainty in LLM-generated text. Drawing on insights from experimental moral psychology and virtue ethics, we define a set of symbolic behavioral rules such as precaution, deference, and responsibility to guide system responses under conditions of epistemic or aleatoric uncertainty. These rules are implemented declaratively and are designed to generate adaptive, context-sensitive explanations even in the absence of precise confidence metrics. The moral principles are encoded as symbolic rules within a lightweight Prolog-based engine, where each uncertainty tag (low, medium, high) activates an ethically aligned system action along with an automatically generated, plain-language rationale. We evaluate the framework through scenario-based simulations that benchmark rule coverage, assess fairness implications, and analyze trust calibration. An interpretive explanation module is integrated to reveal both the assigned uncertainty level and its underlying justification in a transparent and accessible way. We illustrate the framework through hypothetical yet plausible use cases in clinical and legal domains, demonstrating how rule-based moral reasoning can enhance user trust, promote fairness, and improve the interpretability of AIgenerated language. By offering a lightweight, philosophically grounded alternative to probabilistic uncertainty modeling, our approach paves the way for more ethical, human-aligned, and socially responsible natural language generation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".