By adopting a negative ontology where the true target does not objectively exist, the paper defines Democratic Supervision and derives the EL-MIATTs framework for ML evaluation and learning with Multiple Inaccurate True Targets.
Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
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Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision
By adopting a negative ontology where the true target does not objectively exist, the paper defines Democratic Supervision and derives the EL-MIATTs framework for ML evaluation and learning with Multiple Inaccurate True Targets.