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

Abstract

This article philosophically examines how a shift in the assumed ontology of the true target (TT) can lead to a new paradigm for machine learning (ML)-based predictive modelling. By systematically analysing the existence assumption of the TT underlying mainstream ML paradigms, we adopt a negative ontology perspective, explicitly positing that the TT does not objectively exist in the real world as a universally accessible object. On this basis, we define Democratic Supervision as an alternative supervisory principle for ML, in which no single source is assumed to possess an objectively privileged target. We further introduce Multiple Inaccurate True Targets (MIATTs) as an instance-level realization of Democratic Supervision. Building upon MIATTs, we establish the logic-driven generation and assessment for MIATTs construction (recognition with MIATTs), formulate logical assessment formula for evaluation with MIATTs, and develop undefinable true target learning for learning with MIATTs. These components are integrated to formulate the Recognition, Evaluation, Learning with MIATTs (REL-MIATTs) framework. We further characterize REL-MIATTs from the perspective of Cognitive Machine Learning: a single cycle provides an elementary cognitive learning unit, MIATTs introduce complementary supervisory perspectives, and iterative cycles support the evolution of learning through accumulated experience, feedback, and state updating. This provides a basis for human-AI co-evolution. We examine how REL-MIATTs support human-AI co-evolution and adaptive knowledge discovery in a synthetic controlled environment. A real-world application further demonstrates the potential of the framework for supporting individual education and professional development, providing empirical evidence for the feasibility of Democratic Supervision, REL-MIATTs, and its broader implications for continuous human-AI co-evolution.

We add experiments using a synthetic controlled environment to validate Human-AI Co-Evolution and Adaptive Knowledge Discovery in REL-MIATTs, providing a controlled reference for potential real-world applications. Further details of this validation will be presented in a subsequent work