Year: 2026
Type: conference-paper
Abstract: Most work in computational ethics treats annotator disagreement on moral content as noise to be voted away, collapsed into majority vote or the more permissive any-annotator rule which assigns a label as soon as any annotator flags it.We argue this uncertainty should instead be modeled and learned from.We introduce Moral Entropy, a Bayesian framework that estimates a full posterior over the true label and decomposes its entropy into aleatoric uncertainty (irreducible disagreement about the moral content) and epistemic uncertainty (from insufficient or noisy annotation).This lets any heuristic consensus rule be audited against a calibrated ground truth via entropy methods such as cross-entropy/KL, Brier score, and expected calibration error.Running this model on over 100,000 posts from three corpora spanning fifteen discourse domains, we audit the standard aggregation rules against the resulting posterior and reveal systematic differences in moral labels.Across MFTC and MFRC, the any-annotator rule yields mean false-positive rates of 10.2-19.9% and mean false-negative rates of 38.9-54.8%relative to the Bayesian consensus, while majority-vote and two-vote rules miss 63-83% of reference-positive labels.
Author: Maciej Skorski
Institutions: University of Luxembourg, University of Warsaw
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Université du Luxembourg
OpenAlex ID: W7213934137