Data Contradictions Are Uncertainty, Not Noise

This position paper argues that data contradictions in ML training sets should be treated as uncertainty to be quantified, not noise to be cleaned away. The standard ML pipeline treats data quality as preprocessing: find inconsistencies, pick a repair, train on the result. This workflow silently discards information. When two hospital records disagree on a patient's diagnosis, that disagreement reflects genuine ambiguity, and a model trained on one arbitrary resolution is overconfident in exactly the cases where it should be uncertain. We show that the multiplicity of valid data repairs maps naturally to prediction uncertainty: train on each repair, measure prediction disagreement, and the result is an informative confidence signal requiring no architectural changes and no Bayesian machinery. On Adult Income, models trained on different repair strategies disagree with clean-baseline predictions on 2.3% of test instances, concentrated in exactly the subgroups affected by the original contradiction. Of 101 data-cleaning-for-ML papers surveyed by Cote et al., zero treat repair non-uniqueness as an uncertainty signal. Of 51 tabular-ML papers at NeurIPS and ICML 2024-2025, not one engages with the 26-year database theory literature on consistent query answering. The field cleans when it should reason.