Moral Entropy: Auditing Bias and Uncertainty in Moral Judgment
Moral Entropy treats moral-label disagreement as a posterior and finds common vote rules badly misclassify items.
The paper introduces Moral Entropy, a Bayesian framework that keeps a posterior over moral labels and splits entropy into aleatoric and epistemic uncertainty. Consensus rules can be audited with cross-entropy, KL divergence, Brier score, and expected calibration error. Across three corpora and fifteen discourse domains, the any-annotator rule disagrees with the calibrated posterior on about 30 percent of items, mostly false positives. Stricter majority and two-vote rules miss 63 to 83 percent of true positives; on MFTC the reported mean false-positive and false-negative rates are 19.9 and 38.9 percent.
- Moral Entropy splits entropy into aleatoric and epistemic uncertainty.
- Any-annotator rule disagrees with the posterior on about 30 percent of items.
- Majority and two-vote rules miss 63 to 83 percent of true positives.
- On MFTC, any-annotator mean FPR and FNR are 19.9 and 38.9 percent.
From the original article
1 min read at arxiv.orgMost 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 the moment a single annotator flags an item. We argue this uncertainty should instead be modeled and learned from.
By Maciej Skorski · arxiv.org
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