Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision

2026-04-27

AI Summary

This study challenges the foundational assumption in mainstream machine learning that objective ground-truth labels exist, an assumption often violated in real-world scenarios and leading to inaccurate evaluation and learning. Adopting a negative ontological stance—asserting that no single true label exists—the work introduces, for the first time, this philosophical perspective into machine learning through a democratic supervision framework. It proposes representing each instance with multiple imprecise yet authentic truth labels (MIATTs), accompanied by a logic-driven mechanism for generating and evaluating MIATTs, as well as a truth-learning strategy that operates without relying on a predefined ground truth. The resulting EL-MIATTs methodology is validated in real educational settings, demonstrating not only a departure from the conventional single-ground-truth paradigm but also practical utility in supporting personalized education and career development.

Abstract

This article philosophically examines how shifts in assumptions regarding the existence and non-existence of the true target (TT) give rise to new perspectives and insights for machine learning (ML)-based predictive modeling and, correspondingly, proposes a knowledge system for evaluation and learning under Democratic Supervision. By systematically analysing the existence assumption of the TT in current mainstream ML paradigms, we explicitly adopt a negative ontology perspective, positing that the TT does not objectively exist in the real world, and, grounded in this non-existence assumption, define Democratic Supervision for ML. We further present Multiple Inaccurate True Targets (MIATTs) as an instance-level realization of Democratic Supervision. Building upon MIATTs, we derive principles, for the logic-driven generation and assessment of MIATTs, a logical assessment formulation for evaluation with MIATTs, and undefinable true target learning for learning with MIATTs. Based on these components, we establish the evaluation and learning with MIATTs (EL-MIATTs) framework for ML-based predictive modelling. A real-world application demonstrates the potential of the proposed EL-MIATTs framework in supporting education and professional development for individuals, aligning with prior discussions of Democratic Supervision in the fields of education and professional development.

Problem

• True Target
• Negative Ontology
• Democratic Supervision
• Machine Learning
• Predictive Modeling

Innovation

• Negative Ontology
• Democratic Supervision
• Multiple Inaccurate True Targets
• Undefinable True Target Learning
• EL-MIATTs